Reception device

ABSTRACT

It is possible to demodulate a plurality of modulated signals transmitted from a plurality of antennas by using a comparatively small-size circuit with a preferable error ratio characteristic. Partial bit judgment units ( 509, 512 ) demodulates partial bits of the 64 QAM-modulated signal by modifying which of the bits in the 6-bit strings constituting a symbol is to be demodulated depending on in which region of the IQ plane the reception signal point exists. This improves the partial bit error characteristic judged by the partial bit judgment units ( 509, 512 ), which in turn improves reliability of the reduced candidate signal point for use by a likelihood detection unit ( 518 ). As a result, it is possible to improve the error ratio characteristic of the final reception digital signals ( 322, 323 ).

TECHNICAL FIELD

The present invention relates to a receiving apparatus that receives anddemodulates modulated signals transmitted simultaneously from aplurality of antennas and a transmitting apparatus that transmitsmodulated signals simultaneously from a plurality of antennas.

BACKGROUND ART

Hitherto, the technology disclosed in Non-Patent Document 1 has beenknown as a demodulation method using a plurality of antennas. Thecontents disclosed in this Non-Patent Document 1 are briefly describedbelow using an accompanying drawing.

In FIG. 1, in a transmitting apparatus 30, a transmit signal A digitalsignal 1 and transmit signal B digital signal 2 are input to a modulatedsignal generation section 3. Modulated signal generation section 3executes BPSK (Binary Phase Shift Keying), QPSK (Quadrature Phase ShiftKeying), 16QAM (Quadrature Amplitude Modulation), or suchlike modulationon transmit signal A digital signal 1 and transmit signal B digitalsignal 2, thereby obtaining a transmit signal A baseband signal 4 andtransmit signal B baseband signal 5, and sends these signals to a radiosection 6.

Radio section 6 executes predetermined radio processing such asfrequency conversion and amplification on transmit signal A basebandsignal 4 and transmit signal B baseband signal 5, thereby obtaining atransmit signal A modulated signal 7 and transmit signal B modulatedsignal 8, and supplies these signals to an antenna 9 and antenna 10respectively. By this means, transmit signal A modulated signal 7 isemitted as a radio wave from antenna 9, and transmit signal B modulatedsignal 8 is emitted as a radio wave from antenna 10.

In a receiving apparatus 40, a radio section 13 executes radioprocessing such as frequency conversion and amplification on a receivedsignal 12 received by an antenna 11, thereby obtaining a baseband signal14, and sends this signal to a maximum likelihood detection section 19.Similarly, a radio section 17 executes radio processing such asfrequency conversion and amplification on a received signal 16 receivedby an antenna 15, thereby obtaining a baseband signal 18, and sends thissignal to maximum likelihood detection section 19.

By detecting baseband signals 14 and 18, maximum likelihood detectionsection 19 obtains a transmit signal A received digital signal 20 andtransmit signal B received digital signal 21 at this time, maximumlikelihood detection section 19 performs Maximum Likelihood Detection(MLD) as shown in Non-Patent Document 1.

Non-patent Document 1: IEEE WCNC 1999, pp. 1038-1042, September 1999.

DISCLOSURE OF INVENTION Problems to be Solved by the Invention

However, with the configuration in FIG. 1, if 16QAM is performed bymodulated signal generation section 3, for example, when MLD isperformed by maximum likelihood detection section 19, it is necessary tofind the Euclidian distances between 16×16=256 candidate signal pointsand a received signal. Furthermore, if 64QAM is performed by modulatedsignal generation section 3, when MLD is performed by maximum likelihooddetection section 19, it is necessary to find the Euclidian distancesbetween 64×64=4096 candidate signal points and a received signal. Whendetection is performed by means of such computations, while goodreception quality (bit error rate performances) can certainly beachieved, there is a problem in that the computational complexity islarge because of the large number of computations. As described above,this problem becomes more pronounced as the modulation M-ary numberincreases.

It is an object of the present invention to provide a receivingapparatus that can demodulate a plurality of modulated signalstransmitted from a plurality of antennas with a comparatively smallcomputational complexity and good bit error rate performances. It isalso an object of the present invention to provide a transmittingapparatus that forms a transmit signal such that a received signal withgood bit error rate performances can be obtained on the receiving sidewith a comparatively small computational complexity.

Means for Solving the Problems

A receiving apparatus of the present invention for solving theseproblems receives modulated signals transmitted from a transmittingapparatus that transmits different modulated signals from a plurality ofantennas; and employs a configuration that includes: a channelfluctuation estimation section that finds a channel estimate of eachmodulated signal; a partial bit demodulation section that demodulatesonly some bits of a modulated signal using a detection method differentfrom likelihood detection; a signal point reduction section that reducesthe number of candidate signal points using demodulated partial bits anda channel estimate; and a likelihood detection section that performslikelihood detection using a reduced number of candidate signal pointsand a received baseband signal.

According to this configuration, since demodulation of only some bits isperformed by the partial bit demodulation section using a detectionmethod different from likelihood detection, partial bits can be obtainedwith a small amount of computation. Also, likelihood detection isperformed by the likelihood detection section using a reduced number ofcandidate signal points so that the remaining bits can be found with ahigh degree of precision using a small amount of computation. Aslikelihood detection is performed on a partial basis in this way, areceived digital signal with good bit error rate performances can beobtained while reducing the number of computations for finding Euclidiandistances.

A receiving apparatus of the present invention employs a configurationwherein the partial bit demodulation section, in demodulating some bitsof a modulated signal that has undergone 64QAM modulation, changes whichbit in a 6-bit bit string making up one symbol is demodulated as apartial bit according to which area on the IQ plane the relevantreception signal point is present in.

According to this configuration, the bit error performance of a partialbit demodulated by the partial bit demodulation section improves, andtherefore the reliability of reduced candidate signal points used by thelikelihood detection section improves. As a result, the bit errorperformance of final demodulated bits can be improved.

ADVANTAGEOUS EFFECT OF THE INVENTION

According to the present invention, a receiving apparatus can berealized that can demodulate a plurality of modulated signalstransmitted from a plurality of antennas with a comparatively smallcomputational complexity and good bit error rate performances. Also, atransmitting apparatus can be realized that forms a transmit signal suchthat a received signal with good bit error rate performances can beobtained on the receiving side with a comparatively small computationalcomplexity.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram showing a schematic configuration of aconventional multi-antenna transmitting apparatus and receivingapparatus;

FIG. 2 is a block diagram showing the configuration of a transmittingapparatus according to Embodiment 1 of the present invention;

FIG. 3 is a drawing showing frame configurations of Embodiment 1;

FIG. 4 is a block diagram showing the configuration of a receivingapparatus according to Embodiment 1 of the present invention;

FIG. 5 is a block diagram showing the configuration of a signalprocessing section of a receiving apparatus;

FIG. 6 is a drawing showing the relationship of transmitting andreceiving antennas in Embodiment 1;

FIG. 7A is a drawing showing a 16QAM bit arrangement applied tomodulated signal A;

FIG. 7B is a drawing showing a 16QAM bit arrangement applied tomodulated signal B;

FIG. 8 is a drawing showing a sample signal point arrangement ofestimated signal points (candidate signal points) when a 16 QAMmodulated signal A and 16 QAM modulated signal B are received;

FIG. 9A is a drawing showing a 16QAM bit arrangement;

FIG. 9B is a drawing showing an area division method for 16QAM partialbit determination according to Embodiment 1;

FIG. 10 is a drawing showing the signal point state after signal pointreduction according to Embodiment 1;

FIG. 11A is a drawing showing a 16QAM bit arrangement;

FIG. 11B is a drawing showing an area division method for partial bitdetermination of two 16QAM bits;

FIG. 12 is a block diagram showing the configuration of a transmittingapparatus of Embodiment 1;

FIG. 13A is a drawing showing the frame configuration of modulatedsignal A transmitted from the transmitting apparatus in FIG. 12;

FIG. 13B is a drawing showing the frame configuration of modulatedsignal B transmitted from the transmitting apparatus in FIG. 12;

FIG. 14 is a block diagram showing the configuration of a receivingapparatus that receives a signal from the transmitting apparatus in FIG.12;

FIG. 15A is a drawing showing a signal point arrangement by atransmitting apparatus of Embodiment 2;

FIG. 15B is a drawing showing an area division method at the time ofpartial bit determination by a receiving apparatus of Embodiment 2;

FIG. 16 is a block diagram showing another sample configuration of asignal processing section of Embodiment 2;

FIG. 17 is a drawing showing a 64QAM signal point arrangement;

FIG. 18 is a drawing showing a signal point arrangement by atransmitting apparatus of Embodiment 3 and an area division method forpartial bit determination by a receiving apparatus;

FIG. 19 is a drawing showing a signal point arrangement by atransmitting apparatus of Embodiment 3 and an area division method forpartial bit determination by a receiving apparatus;

FIG. 20 is a block diagram showing the configuration of a transmittingapparatus of Embodiment 4;

FIG. 21 is a block diagram showing a configuration of a signalprocessing section of a receiving apparatus of Embodiment 4;

FIG. 22 is a drawing provided to explain computational processing by thesoft decision value calculation section in FIG. 21;

FIG. 23 is a block diagram showing another sample configuration of asignal processing section of Embodiment 4;

FIG. 24 is a block diagram showing a configuration of a coding sectionof Embodiment 5;

FIG. 25A is a drawing showing the configuration of a partial bitdetermination section that determines partial bits of modulated signal Aaccording to Embodiment 5;

FIG. 25B is a drawing showing the configuration of a partial bitdetermination section that determines partial bits of modulated signal Baccording to Embodiment 5;

FIG. 25C is a drawing showing the configuration of a likelihooddetection section of Embodiment 5;

FIG. 26 is a block diagram showing another sample configuration of acoding section of Embodiment 5;

FIG. 27 is a block diagram showing another sample configuration of asignal processing section of a receiving apparatus according toEmbodiment 5;

FIG. 28 is a block diagram showing the configuration of a modulationsection for performing trellis coding modulation according to Embodiment6;

FIG. 29 is a drawing showing an area division method for partial bitdetermination of a BPSK signal;

FIG. 30 is a block diagram showing the configuration of a transmittingapparatus of Embodiment 7;

FIG. 31 is a drawing showing frame configurations of Embodiment 7;

FIG. 32 is a block diagram showing the configuration of a receivingapparatus of Embodiment 7;

FIG. 33 is a block diagram showing a configuration of a signalprocessing section of a receiving apparatus according to Embodiment 7;

FIG. 34 is a block diagram showing another configuration of a signalprocessing section of a receiving apparatus according to Embodiment 7;

FIG. 35 is a drawing provided to explain 1-bit determination processingof Embodiment 8;

FIG. 36 is a flowchart showing the 1-bit determination processingprocedure of Embodiment 8;

FIG. 37 is a drawing provided to explain area division of Embodiment 9;

FIG. 38 is a drawing provided to explain area division of Embodiment 9;

FIG. 39 is a drawing provided to explain Manhattan distance andEuclidian distance according to Embodiment 11;

FIG. 40 is a drawing provided to explain approximation of Euclidiandistances using Manhattan distances according to Embodiment 11;

FIG. 41 is a block diagram showing a sample circuit configuration forlikelihood detection of Embodiment 11;

FIG. 42 is a flowchart showing the partial bit determination processingprocedure of Embodiment 12;

FIG. 43 is a block diagram showing the configuration of a signalprocessing section of a receiving apparatus according to Embodiment 13;

FIG. 44 is a block diagram showing the configuration of a signalprocessing section of a receiving apparatus according to Embodiment 14;and

FIG. 45 is a block diagram showing the configuration of a signalprocessing section of a receiving apparatus according to Embodiment 15.

MODES FOR CARRYING OUT THE INVENTION

Embodiments of the present invention will now be described in detailwith reference to the accompanying drawings.

Embodiment 1

FIG. 2 shows the configuration of a transmitting apparatus of thisembodiment. In transmitting apparatus 100, digital signal 101 is inputto modulation section 102, and digital signal 109 is input to modulationsection 110.

Modulation section 102 has digital signal 101 and frame configurationsignal 118 as input, modulates digital signal 101 in accordance withframe configuration signal 118, and sends baseband signal 103 thusobtained to spreading section 104. Spreading section 104 multipliesbaseband signal 103 by a spreading code, and sends a spread basebandsignal 105 thus obtained to radio section 106. Radio section 106executes frequency conversion, amplification, and so forth on spreadbaseband signal 105, thereby obtaining modulated signal 107. Modulatedsignal 107 is output as a radio wave from an antenna 108.

Modulation section 110 has digital signal 109 and frame configurationsignal 118 as input, modulates digital signal 109 in accordance withframe configuration signal 118, and sends baseband signal 111 thusobtained to spreading section 112. Spreading section 112 multipliesbaseband signal 111 by a spreading code, and sends spread basebandsignal 113 thus obtained to radio section 114. Radio section 114executes frequency conversion, amplification, and so forth on spreadbaseband signal 113, thereby obtaining modulated signal 115. Modulatedsignal 115 is output as a radio wave from an antenna 116.

In the following description, a signal transmitted from antenna 108 isreferred to as modulated signal A, and a signal transmitted from antenna116 is referred to as modulated signal B.

Frame configuration signal generation section 117 outputs informationindicating frame configurations—such as information on the frameconfigurations in FIG. 3, for example—as frame configuration signal 118.

FIG. 3 shows sample frame configurations of modulated signalstransmitted from antennas 108 and 116 of transmitting apparatus 100.Modulated signal A transmitted from antenna 108 and modulated signal Btransmitted from antenna 116 have channel estimation symbols 201 and 203for channel estimation, and data symbols 202 and 204. Transmittingapparatus 100 transmits modulated signal A and modulated signal B withthe frame configurations shown in FIG. 3 at virtually the same time.Channel estimation symbols 201 and 203 for channel estimation can alsobe referred to as pilot symbols, unique words, or preambles.

FIG. 4 shows the configuration of a receiving apparatus of thisembodiment. Receiving apparatus 300 receives signals from two antennas301 and 311.

A radio section 303 has received signal 302 received by antenna 301 asinput, executes frequency conversion, quadrature demodulation, and soforth on received signal 302, and sends baseband signal 304 thusobtained to despreading section 305. Despreading section 305 despreadsbaseband signal 304, and outputs despread baseband signal 306 thusobtained.

Modulated signal A channel fluctuation estimation section 307 hasdespread baseband signal 306 as input, estimates channel fluctuationusing modulated signal A channel estimation symbol 201 in the frameconfiguration in FIG. 3, for example, and sends modulated signal Achannel fluctuation signal 308 thus obtained to signal processingsection 321. Similarly, modulated signal B channel fluctuationestimation section 309 has despread baseband signal 306 as input,estimates channel fluctuation using modulated signal B channelestimation symbol 203 in the frame configuration in FIG. 3, for example,and sends modulated signal B channel fluctuation signal 310 thusobtained to signal processing section 321.

Radio section 313 has received signal 312 received by antenna 311 asinput, executes frequency conversion, quadrature demodulation, and soforth on received signal 312, and sends baseband signal 314 thusobtained to despreading section 315. Despreading section 315 despreadsbaseband signal 314, and outputs despread baseband signal 316 thusobtained.

Modulated signal A channel fluctuation estimation section 317 hasdespread baseband signal 316 as input, estimates channel fluctuationusing modulated signal A channel estimation symbol 201 in the frameconfiguration in FIG. 3, for example, and sends modulated signal Achannel fluctuation signal 318 thus obtained to signal processingsection 321. Similarly, modulated signal B channel fluctuationestimation section 319 has despread baseband signal 316 as input,estimates channel fluctuation using modulated signal B channelestimation symbol 203 in the frame configuration in FIG. 3, for example,and sends modulated signal B channel fluctuation signal 320 thusobtained to signal processing section 321.

Signal processing section 321 has despread baseband signals 306 and 316,modulated signal A channel fluctuation signals 308 and 318, andmodulated signal B channel fluctuation signals 310 and 320 as input, andby performing modulated signal A and B detection, decoding, and soforth, using these signals, obtains modulated signal A digital signal322 and modulated signal B digital signal 323. The detailedconfiguration of signal processing section 321 is shown in FIG. 5, anddetails of its operation will be described later herein.

FIG. 6 shows the relationship between transmitting and receivingapparatuses according to this embodiment. Assume that a signaltransmitted from antenna 108 of transmitting apparatus 100 is designatedTxa(t), and a signal transmitted from antenna 116, Txb(t); a signalreceived by antenna 301 of receiving apparatus 300 is designated Rx1(t),and a signal received by antenna 311, Rx2(t); and propagationfluctuations between the antennas are designated h11(t), h12(t), h21(t),and h22(t). Then the relational expression in the following equationholds true, where t denotes time.

$\begin{matrix}{\begin{pmatrix}{{Rx}\; 1(t)} \\{{Rx}\; 2(t)}\end{pmatrix} = {\begin{pmatrix}{h\; 11(t)} & {h\; 21(t)} \\{h\; 12(t)} & {h\; 22(t)}\end{pmatrix}\begin{pmatrix}{{Txa}(t)} \\{{Txb}(t)}\end{pmatrix}}} & (1)\end{matrix}$

FIG. 7A and FIG. 7B show the signal point arrangements and bitassignments of modulated signal A and modulated signal B when 16QAM(Quadrature Amplitude Modulation) is performed by modulation sections102 and 110. FIG. 7A shows the signal point arrangement and bitassignments of modulated signal A, and FIG. 7B shows the signal pointarrangement and bit assignments of modulated signal B. For bothmodulated signal A and modulated signal B, 4 bits are assigned to onesymbol. In this embodiment, for the sake of explanation, the 4 bitsassigned to one symbol of modulated signal A are designated (Sa0, Sa1,Sa2, Sa3), and the 4 bits assigned to one symbol of modulated signal Bare designated (Sb0, Sb1, Sb2, Sb3). That is to say, (Sa0, Sa1, Sa2,Sa3) and (Sb0, Sb1, Sb2, Sb3) can each have 16 values from (0, 0, 0, 0)to (1, 1, 1, 1).

When modulated signal A and modulated signal B are 16QAM signals asshown in FIG. 7A and FIG. 7B, there are 16×16=256 signal points in amultiplexed received signal. Estimated signal points for these 256signal points in the I-Q plane can be obtained from modulated signal Achannel fluctuation signal 308 and modulated signal B channelfluctuation signal 310 in FIG. 4. An example of this signal pointarrangement is shown in FIG. 8. Black dots in FIG. 8 indicate 256estimated signal points. Reference code 701 indicates a signal point ofdespread baseband signal 306 in FIG. 4. At this time, modulated signal Aand modulated signal B decoding and detection can be performed byfinding the signal point distances between the 256 estimated signalpoints and despread-baseband-signal signal point 701, and seeking theestimated signal point with the smallest distance value. For example,reference code 702 indicates an estimated signal point for which (Sa0,Sa1, Sa2, Sa3, Sb0, Sb1, Sb2, Sb3) is (0, 0, 0, 0, 0, 0, 0, 0), and inthe case shown in FIG. 8, reception point 701 is at the smallestdistance from estimated signal point 702 among the 256 estimated signalpoints, enabling (0, 0, 0, 0, 0, 0, 0, 0) to be obtained as thedetection result.

A drawback with performing detection in this way is that it is necessaryto find the signal point distances between a reception point and all 256estimated signal points, and therefore the computational complexity isextremely large. However, an advantage is that good reception quality(data with good bit error rate performances) can be obtained. On theother hand, a detection method in which the inverse matrix computationof the relational expression of Equation (1) is performed enables thecomputational complexity to be reduced, but has a drawback of poor biterror rate performances.

Receiving apparatus 300 of this embodiment is configured based on thefeatures of both these methods, enabling receive data of high quality(with good bit error rate performances) to be obtained with a smallcomputational complexity.

FIG. 5 shows the detailed configuration of signal processing section321, which is a feature of receiving apparatus 300 of this embodiment.

A separation section 507 has modulated signal A channel fluctuationsignals 308 and 318, modulated signal B channel fluctuation signals 310and 320, and despread baseband signals 306 and 316 as input, and obtainsestimated signals of transmit signals Txa(t) and Txb(t) by performingthe inverse matrix computation of Equation (1). Separation section 507sends thus obtained modulated signal A estimated baseband signal 508 topartial bit determination section 509, and also sends modulated signal Bestimated baseband signal 511 to partial bit determination section 512.

Here, separation section 507 and partial bit determination sections 509and 512 make up partial bit demodulation section 550 that demodulatesonly some bits of modulated signals A and B using a detection methoddifferent from likelihood detection. In this embodiment, a case isdescribed in which Equation (1) inverse matrix computation is performedby separation section 507, but a received signal in which a plurality ofmodulated signals are mixed together may also be separated intomodulated signals A and B by performing MMSE computation, for example,the essential point being that only some bits of modulated signals A andB are demodulated using a detection method different from likelihooddetection.

The operation of partial bit determination sections 509 and 512 will nowbe explained. Partial bit determination section 509 and partial bitdetermination section 512 perform similar operations, with only thesignals processed being different, and therefore the operation ofpartial bit determination section 509 for modulated signal A will bedescribed here. FIG. 9A shows the arrangement of the coordinates of the16 signal points (symbols) of 16QAM. As can be seen, the 4 bits (Sa0,Sa1, Sa2, Sa3) making up one modulated signal A symbol can have a valuefrom (0, 0, 0, 0) to (1, 1, 1, 1) according to the signal pointlocation.

Partial bit determination section 509 has modulated signal A estimatedbaseband signal 508 as input, determines Sa0=1 when modulated signal Aestimated baseband signal 508 is present in area 1 shown in FIG. 9B,Sa0=0 when present in area 2, Sa2=1 when present in area 3, Sa2=0 whenpresent in area 4, and Sa3=1 when present in area 5, and outputs thisinformation as modulated signal A determined partial bit information510. Partial bit determination section 512 has modulated signal Bestimated baseband signal 511 as input, performs the same kind ofoperation as described above, and outputs modulated signal B determinedpartial bit information 513.

The reason for setting the areas that determine 1 bit as shown in FIG.9B is that 1 bit set as shown in FIG. 9B from among Sa0, Sb1, Sa2, andSa3 has a higher probability of being correct than the remaining 3 bits.Therefore, if this 1 bit is determined, there is a low probability ofdegradation of reception quality in subsequent detection.

Next, the operation of signal point reduction sections 514 and 516 willbe explained. Signal point reduction section 514 has modulated signal Achannel fluctuation estimation signal 318, modulated signal B channelfluctuation estimation signal 320, modulated signal A determined partialbit information 510, and modulated signal B determined partial bitinformation 513 as input. If signal point reduction were not performedhere, 256 signal point candidate points would be found from modulatedsignal A channel fluctuation estimation signal 318 and modulated signalB channel fluctuation estimation signal 320 as shown in FIG. 8. However,in this embodiment, by using modulated signal A determined partial bitinformation 510 and modulated signal B determined partial bitinformation 513, as described above, from bit-by-bit determinationinformation (a total of 2 bits), only 8−2=6 bits (64 signal points) areundetermined of the 8 bits (256 signal points). For example, assume thatSa0=1 information is input to signal point reduction section 514 asmodulated signal A determined partial bit information 510, and Sb0=0information is input to signal point reduction section 514 as modulatedsignal B determined partial bit information 513. Signal point reductionsection 514 then eliminates signal points that do not have Sa0=1 andSb0=0 values from among the 256 signal points (FIG. 8). By this means,the number of candidate signal points can be reduced to 64, and signalpoint reduction section 514 outputs information of these 64 signalpoints as post-reduction signal point information 515. Signal pointreduction section 516 has modulated signal A channel fluctuation signal308, modulated signal B channel fluctuation signal 310, modulated signalA determined partial bit information 510, and modulated signal Bdetermined partial bit information 513 as input, performs the same kindof operation as described above, and outputs post-reduction signal pointinformation 517.

A likelihood detection section 518 has despread baseband signals 306 and316, and post-reduction signal point information 515 and 517, as input.Then the state in FIG. 10 is obtained from post-reduction signal pointinformation 515 and despread baseband signal 316. In FIG. 10, despreadbaseband signal 316 is the signal point indicated by reference code 701,and post-reduction signal point information 515 comprises the 64 signalpoints indicated by black dots. Likelihood detection section 518 thenfinds the signal point distances between the 64 candidate signal pointsand despread-baseband-signal signal point 701. That is to say,likelihood detection section 518 finds a branch metric. This is namedbranch metric X. Similarly, likelihood detection section 518 finds thesignal point distances between the 64 candidate signal points anddespread-baseband-signal signal point 701 from post-reduction signalpoint information 517 and despread baseband signal 306. That is to say,likelihood detection section 518 finds a branch metric. This is namedbranch metric Y.

Then likelihood detection section 518 finds the 8-bit sequence with thehighest likelihood using branch metric X and branch metric Y, andoutputs this as modulated signal A digital signal 322 and modulatedsignal B digital signal 323. In the example in FIG. 5, likelihooddetection section 518 separates and outputs (in parallel) modulatedsignal A and modulated signal B digital signals 322 and 323, butmodulated signal A and modulated signal B digital signals may also bebundled and output (in series) as a single digital signal.

Thus, according to this embodiment, by providing partial bitdemodulation section 550 that determines partial bits from among aplurality of bits that make up one symbol of each modulated signal usinga detection method different from likelihood detection, signal pointreduction sections 514 and 516 that reduce the number of candidatesignal points using the determined partial bits, and likelihooddetection section 518 that obtains received digital signals 322 and 323by performing likelihood detection based on the Euclidian distancesbetween reduced candidate signal points and a reception point, areceiving apparatus 300 can be realized that enables bit error rateperformances to be effectively improved with a comparatively smallcomputational complexity. That is to say, as a reduced number ofcandidate signal points are used by likelihood detection section 518,the number of computations for finding Euclidian distances is reduced,enabling the computational complexity to be decreased. Also, as partialbits found based on inverse matrix computation results are only bitsunlikely to be erroneous, degradation of bit error rate performances dueto inverse matrix computation can be greatly suppressed compared with acase in which likelihood decoding of all bits is performed based oninverse matrix computation results.

(i) Another Sample Configuration of a Partial Bit Determination Section

In the above embodiment, a case has been described in which a reductionin the number of candidate signal points of a total of 2 bits isperformed by signal point reduction sections 514 and 516 respectively byhaving bit determination performed one bit at a time by partial bitdetermination sections 509 and 512. Here, a method and configurationwill be described whereby a reduction in the number of candidate signalpoints of a total of 4 bits is performed by signal point reductionsections 514 and 516 respectively by having bit determination performed2 bits at a time by partial bit determination sections 509 and 512.

FIG. 11A and FIG. 11B show an example of a determination method fordetermining 2 bits by partial bit determination sections 509 and 512 inFIG. 8. Partial bit determination section 509 and partial bitdetermination section 512 perform similar operations, with only thesignals processed being different, and therefore the operation ofpartial bit determination section 509 for modulated signal A will bedescribed here. FIG. 11A shows the arrangement of the coordinates of the16 signal points (symbols) of 16QAM. As can be seen, the 4 bits (Sa0,Sa1, Sa2, Sa3) making up one modulated signal A symbol can have anyvalue from (0, 0, 0, 0) to (1, 1, 1, 1) according to the signal pointlocation.

Partial bit determination section 509 has modulated signal A estimatedbaseband signal 508 as input, determines Sa0=0 and Sa2=1 when modulatedsignal A estimated baseband signal 508 is present in area 1 bounded bydotted lines in FIG. 11B, Sa1=1 and Sa2=1 when present in area 2, Sa0=1and Sa2=1 when present in area 3, Sa0=0 and Sa3=1 when present in area4, Sa1=1 and Sa3=1 when present in area 5, Sa0=1 and Sa3=1 when presentin area 6, Sa0=0 and Sa2=0 when present in area 7, Sa1=1 and Sa2=0 whenpresent in area 8, and Sa0=1 and Sa2=0 when present in area 9. Partialbit determination section 509 then outputs this information as modulatedsignal A determined partial bit information 510. Partial bitdetermination section 512 has modulated signal B estimated basebandsignal 511 as input, performs the same kind of operation as describedabove, and outputs modulated signal B determined partial bit information513.

The reason for setting the areas that determine 2 bits as shown in FIG.11B is that 2 bits set as shown in FIG. 11B from among Sa0, Sb1, Sa2,and Sa3 have a higher probability of being correct than the remaining 2bits. Therefore, if these 2 bits are determined, there is a lowprobability of degradation of reception quality in subsequent detection.

Signal point reduction section 514 performs candidate signal pointreduction by carrying out the same kind of operations as describedabove. At this time, since modulated signal B determined partial bitinformation 513 is composed of 2 bits, only 8−4=4 bits (16 signalpoints) are undetermined of the 8 bits (256 signal points). By thismeans, the number of candidate signal points can be reduced to 16.Information of these 16 signal points forms post-reduction signal pointinformation. Therefore, branch metric calculation can be further reducedin likelihood detection section 518, and the computational complexitycan be further decreased However, as the number of bits determined bypartial bit determination sections 509 and 512 increases, receptionquality degrades.

(ii) Application to a Multicarrier System

A sample configuration will be described here for a case in which thepresent invention is applied to a multicarrier system. A case in whichOFDM (Orthogonal Frequency Division Multiplexing) scheme is used as amulticarrier system will be described as an example.

FIG. 12 shows the configuration of a transmitting apparatus. Intransmitting apparatus 1100, a digital signal 1101 is input to amodulation section 1102, and a digital signal 1111 is input to amodulation section 1112.

Modulation sections 1102 and 1112 have digital signals 1101 and 1111,and a frame configuration signal 1122, as input, modulate digitalsignals 1101 and 1111 in accordance with frame configuration signal1122, and send baseband signals 1103 and 1113 thus obtained toserial/parallel conversion sections (S/Ps) 1104 and 1114.Serial/parallel conversion sections 1104 and 1114 performserial/parallel conversion of baseband signals 1103 and 1113respectively, and send parallel signals 1105 and 1115 thus obtained toinverse Fourier transform sections (idft's) 1106 and 1116 respectively.Inverse Fourier transform sections 1106 and 1116 execute inverse Fouriertransform processing on parallel signals 1105 and 1115 respectively, andsend post-inverse-Fourier-transform signals 1107 and 1117 thus obtainedto radio sections 1108 and 1118 respectively. Radio sections 1108 and1118 execute frequency conversion, signal amplification, and so forth onpost-inverse-Fourier-transform signals 1107 and 1117 respectively,thereby obtaining modulated signals 1109 and 1119. Modulated signals1109 and 1119 are output as radio waves from antennas 1110 and 1120respectively.

By this means, modulated signal 1109 (modulated signal A) and modulatedsignal 1119 (modulated signal B), which are OFDM signals, aretransmitted from antennas 1110 and 1120 respectively.

Here, a frame configuration signal generation section 1121 outputs frameconfiguration information as frame configuration signal 1122. Sampleframe configurations are shown in FIG. 13A and FIG. 13B. In FIG. 13A andFIG. 13B, frame configurations are represented on time-frequency axes.FIG. 13A shows a frame configuration of modulated signal A, and FIG. 13Bshows a frame configuration of modulated signal B. As an example, a caseis shown in which a frame is composed of carrier 1 through carrier 5. Itis assumed that symbols of the same time slot are transmitted at thesame time. Pilot symbols 1201 indicated by hatching are symbols forperforming channel estimation on the receiving side. Although thesesymbols are referred to here as pilot symbols, they may also be givenanother designation such as “preamble,” and need only be symbols thatenable channel estimation to be performed. Blanks indicated by referencecode 1202 denote data symbols.

FIG. 14 shows the configuration of a receiving apparatus. Receivingapparatus 300 receives signals by means of two antennas 1301 and 1311.

A radio section 1303 has a received signal 1302 received by antenna 1301as input, executes frequency conversion and so forth on received signal1302, and sends a baseband signal 1304 thus obtained to a Fouriertransform section (dft) 1305. Fourier transform section 1305 performsFourier transform processing on baseband signal 1304, and outputs apost-Fourier-transform signal 1306 thus obtained.

A modulated signal A channel fluctuation estimation section 1307 haspost-Fourier-transform signal 1306 as input, finds modulated signal Achannel fluctuation for carrier 1 through carrier 5 using modulatedsignal A pilot symbols 1201 in FIG. 13A, and outputs a modulated signalA channel fluctuation signal group 1308 (composed of estimation signalsfor carrier 1 through carrier 5). Similarly, a modulated signal Bchannel fluctuation estimation section 1309 has post-Fourier-transformsignal 1306 as input, finds modulated signal B channel fluctuation forcarrier 1 through carrier 5 using modulated signal B pilot symbols 1201in FIG. 13B, and outputs a modulated signal B channel fluctuation signalgroup 1310 (composed of estimation signals for carrier 1 through carrier5).

Similarly, a radio section 1313 has a received signal 1312 received byantenna 1311 as input, executes frequency conversion and so forth onreceived signal 1312, and sends a baseband signal 1314 thus obtained toa Fourier transform section (dft) 1315. Fourier transform section 1315performs Fourier transform processing on baseband signal 1314, andoutputs a post-Fourier-transform signal 1316 thus obtained.

A modulated signal A channel fluctuation estimation section 1317 haspost-Fourier-transform signal 1316 as input, finds modulated signal Achannel fluctuation for carrier 1 through carrier 5 using modulatedsignal A pilot symbols 1201 in FIG. 13A, and outputs a modulated signalA channel fluctuation signal group 1318 (composed of estimation signalsfor carrier 1 through carrier 5). Similarly, a modulated signal Bchannel fluctuation estimation section 1319 has post-Fourier-transformsignal 1316 as input, finds modulated signal B channel fluctuation forcarrier 1 through carrier 5 using modulated signal B pilot symbols 1201in FIG. 13B, and outputs a modulated signal B channel fluctuation signalgroup 1320 (composed of estimation signals for carrier 1 through carrier5).

A signal processing section 1321 has post-Fourier-transform signals 1306and 1316, modulated signal A channel fluctuation signal groups 1308 and1318, and modulated signal B channel fluctuation signal groups 1310 and1320 as input, and by performing modulated signal A and B decoding,detection, and so forth, using these signals, obtains a modulated signalA digital signal 1322 and modulated signal B digital signal 1323.

Signal processing section 1321 may have the same kind of configurationas signal processing section 321 shown in FIG. 5. Thus, modulated signalA channel fluctuation estimation group 1308 is input instead ofmodulated signal A channel fluctuation signal 308 in FIG. 5, modulatedsignal B channel fluctuation estimation group 1310 is input instead ofmodulated signal B channel fluctuation signal 310,post-Fourier-transform signal 1306 is input instead of despread basebandsignal 306, modulated signal A channel fluctuation estimation group 1318is input instead of modulated signal A channel fluctuation signal 318,modulated signal B channel fluctuation estimation group 1320 is inputinstead of modulated signal B channel fluctuation signal 320, andpost-Fourier-transform signal 1316 is input instead of despread basebandsignal 316.

Assuming, for example, that separation section 507 has modulated signalA channel fluctuation estimation groups 501 and 504, modulated signal Bchannel fluctuation estimation groups 502 and 505, andpost-Fourier-transform signals 503 and 506 as input, inverse matrixcomputation is executed for each carrier based on Equation (1), andmodulated signal A estimated baseband signal 508 and modulated signal Bestimated baseband signal 511 are output in accordance with thefrequency-time axis frame configurations in FIG. 13A and FIG. 13B.

Then partial bit determination sections 509 and 512 determine partialbits in the same way as described above for each carrier. Signal pointreduction sections 514 and 516 also perform signal point reduction inthe same way as described above for each carrier, and likelihooddetection section 518 also performs likelihood detection for eachcarrier. By this means, OFDM modulated signal A and B digital signals1322 and 1323 are obtained. In this way, the present invention can alsobe implemented for a multicarrier system such as OFDM scheme.

Embodiment 2

In this embodiment, a method of signal point arrangement in the I-Qplane is described that simplifies division in the case of 2-bit partialdetermination and greatly improves reception quality compared withEmbodiment 1. Although the description here mainly refers to modulatedsignal A, the same kind of processing can also be performed formodulated signal B.

The general configurations of a transmitting apparatus and receivingapparatus arc similar to those in Embodiment 1. Embodiment 2 differsfrom Embodiment 1 in the configuration of the modulation sections of thetransmitting apparatus, and the configuration of the partial bitdetermination sections and signal point reduction sections of thereceiving apparatus.

FIG. 15A shows a sample signal point arrangement by a transmittingapparatus of this embodiment, and FIG. 15B shows the partial bitdetermination method used by a receiving apparatus of this embodiment.That is to say, the kind of signal point mapping shown in FIG. 15A isperformed by modulation sections 102 and 110 in FIG. 1 and modulationsections 1102 and 1112 in FIG. 12. Also, partial bits are determined bypartial bit determination sections 509 and 512 in FIG. 5 by performingthe kind of area division shown in FIG. 15B.

As shown in FIG. 15A, a modulation section of this embodiment takes 4signal points as 1 set, and performs modulation processing (mapping) sothat the distances between the 4 signal points in 1 set are small, butdistances between sets are large. Also, a modulation section makes thedistances between the 4 signal points in 1 set equal, and also makes thedistances between sets equal. In this way, a modulation section arrangessignal points so that an area can easily be divided into first throughfourth quadrants.

By this means, 2 bits that are common within a set composed of 4 signalpoints can easily be demodulated on the receiving side. That is to say,since distances between signal points in a set are small and signalpoint distances between sets are large, the set (quadrant) in which areception point is included can be determined easily and accurately,enabling 2-bit partial determination to be performed easily andaccurately.

Specifically, when a received baseband signal is present in area 1 inthe I-Q plane shown in FIG. 15B, the 2 bits Sa0=1 and Sa2=1 common tothe 4 signal points of area 1 are determined to be partial bits; when areceived baseband signal is present in area 2, the 2 bits Sa0=0 andSa2=1 common to the 4 signal points of area 2 are determined to bepartial bits; when a received baseband signal is present in area 3, the2 bits Sa0=0 and Sa2=0 common to the 4 signal points of area 3 aredetermined to be partial bits; and when a received baseband signal ispresent in area 4, the 2 bits Sa0=1 and Sa2=0 common to the 4 signalpoints of area 4 are determined to be partial bits.

Partial bit determination section 509 in FIG. 5 outputs information ofthese determined 2 bits as modulated signal A determined partial bitinformation 510. The same kind of processing is also performed formodulated signal B by partial bit determination section 512.

Using the 4-bit information determined by partial bit determinationsections 509 and 512, signal point reduction sections 514 and 516 inFIG. 5 reduce the 256 candidate signal points to 16 candidate signalpoints as described above in Embodiment 1.

Thus, according to this embodiment, in modulation sections 102, 110,1102, and 1112 of transmitting apparatuses 100 and 1100, by performingsignal point mapping of transmit bits whereby signal points are dividedinto a plurality of signal point sets on the IQ plane, and the minimumdistance between signal points in a signal point set is made smallerthan the minimum signal point distance between signal point sets, aneffect can be obtained of enabling partial bit determination to beperformed easily and accurately on the receiving side.

In addition, by making the distances between the 4 signal points in 1set equal, and also making the distances between sets equal, the ratioof maximum transmit power to average transmit power is reduced. By thismeans, the linear amplifier requirements of the transmitting poweramplifier are lessened, and an effect of enabling power consumption tobe reduced is also obtained. The same is also true when this embodimentis applied to a 64-value modulation method.

In Embodiment 1 and this embodiment, a case has been described in whichthe signal point arrangements of modulated signal A and modulated signalB are the same, but similar effects can also be obtained when the signalpoint arrangements of modulated signal A and modulated signal B aredifferent.

For example, on the transmitting side, the modulated signal A signalpoint arrangement may be set as shown in FIG. 15A, while the modulatedsignal B signal point arrangement is set as shown in FIG. 9A. Then, onthe receiving side, a total of 3 bits are determined by determining 2bits by means of partial bit determination section 509 for modulatedsignal A in FIG. 5, and determining 1 bit by means of partial bitdetermination section 512 for modulated signal B. Signal point reductionsections 514 and 516 then reduce the 256 candidate signal points to 32signal points using this determined 3-bit partial bit information.

A method is also possible whereby only modulated signal A partial bitsare determined on the receiving side. The configuration of signalprocessing section 321 for implementing this method is shown in FIG. 16.In this example, modulated signal A signal points are arranged as shownin FIG. 15A for ease of partial bit determination. Partial bitdetermination section 509 in FIG. 16 performs partial bit determinationof 2 bits of modulated signal A based on the criteria in FIG. 15B.Signal point reduction sections 514 and 516 reduce the 256 candidatesignal points to 64 candidate signal points using the determined 2 bits.Likelihood detection section 518 performs likelihood detection byfinding the Euclidian distances between the 64 signal points and areceived baseband signal.

Determining only partial bits for one modulated signal in this wayenables the configuration of the partial bit determination section to besimplified, allowing the computational complexity to be reducedaccordingly. This kind of configuration is particularly effective when asignal point arrangement is used whereby partial bit determination iseasier for one modulated signal than for the other.

Embodiment 3

In this embodiment, an actual signal point arrangement method andpartial bit determination method when using 64-value M-ary modulation asthe modulation method are described. The general configurations of atransmitting apparatus and receiving apparatus are similar to those inEmbodiment 1 and Embodiment 2, except that the modulation method ischanged from modulation which has 16 signal points to modulation whichhas 64 signal points.

FIG. 17 shows the 64QAM signal point arrangement in the I-Q plane. Areceiving apparatus of this embodiment, 1 bit is determined by each ofpartial bit determination sections 509 and 512 in FIG. 5 by performingarea division so that the bit with the lowest probability of beingerroneous among 6 bits is determined. Then the number of candidatesignal points is reduced to 1024 by reducing 2-bit signal points from64×64=4096 candidate signal points by signal point reduction sections514 and 516. Likelihood detection section 518 performs likelihooddetection by finding the Euclidian distances between each of the 1024candidate signal points and a reception point.

Also, in the receiving apparatus, if area division is performed bypartial bit determination sections 509 and 512 so that 2 bits aredetermined, and the respective 2-bit partial bits are determined, thenumber of candidate signal points can be reduced to 256. If areadivision is performed so that 3 bits are determined, and the respective3-bit partial bits are determined, the number of candidate signal pointscan be reduced to 64. Furthermore, if area division is performed so that4 bits are determined, and the respective 4-bit partial bits aredetermined, the number of candidate signal points can be reduced to 16.Thus, the greater the number of bits determined by partial bitdetermination sections 509 and 512 is made, the smaller the number ofcandidate signal points for performing likelihood detection can be made,enabling the amount of computation to be reduced. However, drawbacks arethat the greater the number of bits determined by partial bitdetermination sections 509 and 512 is made, the more bit error rateperformances degrade, and, as with 16QAM in Embodiment 1, the morecomplicated area division becomes.

Thus, in this embodiment, the kind of signal point arrangement shown inFIG. 18 is proposed as a more desirable 64-value M-ary modulation signalpoint arrangement. The basic concept of the signal point arrangement inFIG. 18 is the same as that described in Embodiment 2. That is to say,modulation processing (mapping) is performed whereby signal points aredivided into a plurality of sets, and the minimum Euclidian distancebetween sets is made greater than the minimum Euclidian distance betweensignal points within a set.

Specifically, 16 signal points are taken as 1 set, and modulationprocessing (mapping) is performed so that the distances between the 16signal points are small, but distances between sets are large. Also, amodulation section makes the distances between the 16 signal points in 1set equal, and also makes the distances between sets equal. In this way,a modulation section arranges signal points so that an area can easilybe divided into first through fourth quadrants.

By this means, 2 bits that are common within a set composed of 16 signalpoints can easily be demodulated on the receiving side. That is to say,since distances between signal points in a set are small and signalpoint distances between sets are large, the set (quadrant) in which areception point is included can be determined easily and accurately,enabling 2-bit partial determination to be performed easily andaccurately.

In this embodiment, the signal point arrangement shown in FIG. 19 isproposed as another desirable signal point arrangement for 64-valueM-ary modulation. FIG. 19 shows a 64-value M-ary modulation signal pointarrangement suitable for determining 4-bit partial bits for eachmodulated signal. The basic concept of this signal point arrangement, asin the case described above, is that modulation processing (mapping) isperformed whereby signal points are divided into a plurality of sets,and the minimum Euclidian distance between sets is made greater than theminimum Euclidian distance between signal points within a set.

Specifically, 4 signal points are taken as 1 set, and modulationprocessing (mapping) is performed so that the distances between the 4signal points within 1 set are small, but distances between sets arelarge. In this way, signal points are arranged so that an area caneasily be divided into areas 1 through 16.

By this means, 4 bits that are common within a set composed of 16 signalpoints can easily be demodulated on the receiving side. That is to say,since distances between signal points in a set are small and signalpoint distances between sets are large, the set (area 1 to 16) in whicha reception point is included can be determined easily and accurately,enabling 4-bit partial determination to be performed easily andaccurately.

Thus, according to this embodiment, when different 64-value M-arymodulation signals are transmitted from a plurality of antennas, byperforming modulation (mapping) processing whereby signal points of 64values are divided into a plurality of sets, and the minimum Euclidiandistance between sets is made larger than the minimum Euclidian distancebetween signal points in a set, easy and accurate partial bitdetermination processing and signal point reduction processing can beperformed on the receiving side, enabling a received signal with goodbit error rate performances to be obtained on the receiving side with acomparatively small computational complexity.

As also explained with regard to Embodiment 2, the method of thisembodiment is not limited to a case in which the signal pointarrangements of modulated signal A and modulated signal B are the same,and may also be implemented even in a case in which modulated signal Aand modulated signal B signal points are arranged differently, and thenumber of partial bits determined for modulated signal A and modulatedsignal B are different.

Embodiment 4

In this embodiment, a soft decision value calculation method isdescribed that is suitable for a case in which convolutional coding orturbo coding is performed on the transmitting side, and soft decisiondecoding is performed on the receiving side, in addition toimplementation of the configurations in Embodiments 1 through 3. Whilethis embodiment can basically be applied to cases in which any of thesignal point arrangements described in the above embodiments are used, acase will be described here, by way of example, in which the signalpoint arrangement shown in FIG. 15A is implemented on the transmittingside.

FIG. 20, in which parts corresponding to those in FIG. 2 are assignedthe same codes as in FIG. 2, shows the configuration of a transmittingapparatus of this embodiment. In transmitting apparatus 1900, a transmitdigital signal 1901 is input to a coding section 1902. Coding section1902 executes convolutional coding on transmit digital signal 1901 andthereby obtains a coded digital signal 101 and coded digital signal 109,and sends these signals to modulation sections 102 and 110.

The overall configuration of a receiving apparatus is as shown in FIG.4. In this embodiment, signal processing section 321 in FIG. 4 isconfigured as signal processing section 2000 in FIG. 21. Parts in FIG.21 corresponding to those in FIG. 5 are assigned the same codes as inFIG. 5.

Signal processing section 2000 of this embodiment has a soft decisionvalue calculation section 2001. Soft decision value calculation section2001 has post-reduction signal point information 515 and 517, anddespread baseband signals 503 and 506, as input, obtains soft decisionvalue signal 2002 using these signals, and sends soft decision valuesignal 2002 to determination section 2003. Determination section 2003obtains digital signal 2004 by decoding soft decision value signal 2002.

The processing performed by soft decision value calculation section 2001and determination section 2003 will be described using FIG. 22.

Assume, for example, that transmitting apparatus 1900 in FIG. 20transmits modulated signals using the kind of signal point arrangementshown in FIG. 15A, and that receiving apparatus 300 has received thesemodulated signals.

Then, in signal processing section 2000 in FIG. 21, partial bitdetermination section 509 determines 2 bits Sa0 and Sa2 of modulatedsignal A based on the area divisions in the signal point arrangement inFIG. 15B, and outputs these as partial bit information 510, andsimilarly, partial bit determination section 512 determines 2 bits Sb0and Sb2 of modulated signal B based on the area divisions in the signalpoint arrangement in FIG. 15B, and outputs these as partial bitinformation 513.

Using the 4-bit information from partial bit determination sections 509and 512, signal point reduction section 514 finds 16 signal points from16×16=256 signal points, and sends these to soft decision valuecalculation section 2001 as post-reduction signal point information 515.Similarly, signal point reduction section 516 sends 16-signal-pointinformation to soft decision value calculation section 2001 aspost-reduction signal point information 517.

Here, as an example, it is assumed that the modulated signal A partialbits determined by partial bit determination section 509 are Sa0=0 andSa2=0, and the modulated signal B partial bits determined by partial bitdetermination section 512 are Sb0=0 and Sb2=0. At this time, softdecision value calculation section 2001 performs the calculations inFIG. 22 using post-reduction signal point information 515 and despreadbaseband signal 316.

(Step ST1)

First, the squares, for example, of the Euclidian distances between the16 signal points of post-reduction signal point information 515 and thedespread baseband signals are found. Here, the squares of Euclidiandistances are represented by the function D(Sa0, Sa2, Sb0, Sb2, Sa1,Sa3, Sb1, Sb3). Then, since Sa0=0, Sa2=0, Sb0=0, and Sb2=0 in thisexample, 16 values are found for which Sa1, Sa3, Sb1, and Sb3 are 0 or 1in D(0, 0, 0, 0, Sa1, Sa3, Sb1, Sb3). Posterior probability can also befound using this.

(Step ST2)

Next, the maximum value is found from the 16 values of D(0, 0, 0, 0,Sa1, Sa3, Sb1, Sb3). The maximum value at this time is designated Dmax.

(Step ST3)

Lastly, the values of the squares of the Euclidian distances of the 240signal points other than the 16 signal points for which the square ofthe Euclidian distance has actually been found are all taken to be Dmax.In this example, the values from D(0, 0, 0, 1, 0, 0, 0, 0) to D(1, 1, 1,1, 1, 1, 1, 1) are all taken to be Dmax. That is to say, since theEuclidian distances to the 240 signal points other than the 16 signalpoints for which the square of the Euclidian distance has actually beenfound can be considered to be greater than the maximum value of thesquares of the Euclidian distances of the 16 signal points, Dmax, thesquares of the Euclidian distances of these signal points are uniformlyset to Dmax. By this means, the squares of the Euclidian distances of256 points can easily be obtained by making effective use of the squaresof the Euclidian distances of 16 signal points.

Then soft decision value calculation section 2001 outputs the value ofthe square of the Euclidian distances of these 256 points (branchmetric) as soft decision value signal 2002.

Determination section 2003 has soft decision value signal 2002 as input,finds a path metric from the branch metric, decodes this, and outputsdigital signal 2004.

Thus, according to signal processing section 2000, a soft decision valuecan easily be obtained for all candidate signal points by obtaining softdecision values for all candidate signal points by calculating only theEuclidian distances between reduced candidate signal points and areception point, and setting all the Euclidian distances between othersignal points and the reception point as maximum value Dmax of theaforementioned found Euclidian distances.

FIG. 23, in which parts corresponding to those in FIG. 21 are assignedthe same codes as in FIG. 21, shows another configuration of a signalprocessing section of this embodiment. Signal processing section 2200has a weighting factor calculation section 2201.

Weighting factor calculation section 2201 has modulated signal A channelfluctuation signals 308 and 318, and modulated signal B channelfluctuation signals 310 and 320, as input, and finds a weighting factorcorresponding to a degree of reliability that is multiplied by a branchmetric. Here, when separation section 507 separates signals byperforming the computations in Equation (1), for example, it issufficient for weighting factor calculation section 2201 to find aweighting factor corresponding to the precision of signal separation.Specifically, weighting factor calculation section 2201 can find theminimum power of an eigenvalue of the matrix in Equation (1), forexample, as shown in “Soft-decision decoder employing eigenvalue ofchannel matrix in MIMO systems” IEEE PIMRC2003, pp. 1703-1707, September2003, and output this as a weighting factor signal 2202.

Soft decision value calculation section 2001 has post-reduction signalpoint information 515 and 517, despread baseband signals 306 and 316,and weighting factor signal 2202 as input, and obtains soft decisionvalue signal 2002 by multiplying a found branch metric by a weightingfactor.

Multiplying a branch metric by a weighting factor in signal processingsection 2200 in this way enables bit error rate performances to begreatly improved. In the above description, a case has been referred toin which the minimum power of an eigenvalue is used as a weightingfactor, but a weighting factor is not limited to this.

Also, in this embodiment, a case has been described in whichconvolutional coding is used, but this embodiment is not limited to thiscase, and can also be similarly implemented in a case in which turbocoding, low-density parity coding, or the like is used. Furthermore,this embodiment can also be similarly implemented when a function suchas interleaving, which changes the signal order, or puncturing, whichperforms partial signal elimination and reduces redundancy, is provided.This is also true for other embodiments.

Also, in this embodiment, an example has been described in which thesquares of Euclidian distances are found and a soft decision value isfound on this basis, but this embodiment can also be applied to a casein which a soft decision value is found on the basis of a differentlikelihood. This also applies to other embodiments. Furthermore, apartfrom the method of finding a soft decision value described in thisembodiment, a method may be used whereby the SNR after separation isfound using step S1, and taking this as a prior probability, a softdecision value is found using a prior probability and posteriorprobability. This also applies to other embodiments.

Embodiment 5

In this embodiment, a more suitable coding (convolutional coding orturbo coding) method is described for use when performing processingthat reduces candidate signal points by partial bit reduction on thereceiving side as described in the above embodiments.

The general configuration of a transmitting apparatus is as shown inFIG. 20. In this embodiment, it is assumed by way of example thatmodulation sections 102 and 110 perform modulation which has 16 signalpoints using the kind of signal point arrangement shown in FIG. 15A. Thegeneral configuration of a receiving apparatus is as shown in FIG. 4.

FIG. 24 shows the configuration of a coding section of this embodiment.That is to say, coding section 2300 in FIG. 24 is used as coding section1902 in FIG. 20. Coding section 2300 has (Sa0, Sa2) coding section 2302,(Sa1, Sa3, Sb1, Sb3) coding section 2304, and (Sb0, Sb2) coding section2306. Coding sections 2302, 2304, and 2306 have digital signal 1901 asinput, and perform coding processing on the respective specific bits.

That is to say, (Sa0, Sa2) coding section 2302 codes bits Sa0 and Sa2contained in digital signal 1901, and outputs bit Sa0 and Sa2 codinginformation 2303; (Sa1, Sa3, Sb1, Sb3) coding section 2304 codes bitsSa1, Sa3, Sb1, and Sb3 contained in digital signal 1901, and outputs bitSa1, Sa3, Sb1, and Sb3 coding information 2305; and (Sb0, Sb2) codingsection 2306 codes bits Sb0 and Sb2 contained in digital signal 1901,and outputs bit Sb0 and Sb2 coding information 2307.

Executing coding processing in predetermined bit units in this wayenables error correction decoding processing to be performed in thosebit units on the receiving side. A particular aspect of the suitabilityof this embodiment is that performing coding processing in bit units forwhich partial bit determination is performed on the receiving sideenables error correction decoding processing to be performed in partialbit units.

(Sa0, Sa1, Sa2, Sa3) signal generation section 2308 has Sa0 and Sa2coding information 2303 and Sa1, Sa3, Sb1, and Sb3 coding information2305 as input, generates Sa0, Sa1 Sa2, and Sa3 signals, and outputsthese as coded digital signal 101.

Similarly, (Sb0, Sb1, Sb2, Sb3) signal generation section 2310 has Sa1,Sa3, Sb1, and Sb3 coding information 2305 and Sb0 and Sb2 codinginformation 2307 as input, generates Sb0, Sb1, Sb2, and Sb3 signals, andoutputs these as coded digital signal 109.

Next, the configuration of a receiving apparatus that receives suchtransmit signals will be described. The general configuration of areceiving apparatus of this embodiment is as shown in FIG. 4. Theconfiguration of signal processing section 321 of receiving apparatus300 is as shown in FIG. 5. In this embodiment, partial bit determinationsection 509 of signal processing section 321 is configured as shown inFIG. 25A, partial bit determination section 512 is configured as shownin FIG. 25B, and likelihood detection section 518 is configured as shownin FIG. 25C.

(Sa0, Sa2) decoding section 2402 in FIG. 25A has modulated signal Aestimated baseband signal 508 as input, obtains decoded bits Sa0 and Sa2by decoding this signal, and outputs these bits as modulated signal Adetermined partial bit information 510.

(Sb0, Sb2) decoding section 2405 in FIG. 25B has modulated signal Bestimated baseband signal 511 as input, obtains decoded bits Sb0 and Sb2by decoding this signal, and outputs these bits as modulated signal Bdetermined partial bit information 513.

Implementing error correction coding in partial bit units in this wayenables reception quality to be greatly improved. That is to say, ifthere is an error in partial bit determination, an erroneous signalpoint is selected during signal point reduction, and therefore theprobability of an error occurring in determination of the remaining bitsis extremely high. In contrast, in this embodiment, the implementationof error correction coding in partial bit units enables the possibilityof being able to decode partial bits correctly to be increased, enablingthe possibility of selecting an erroneous signal point during signalpoint reduction to be decreased.

It is still more desirable for coding with higher error correctioncapability than (Sa1, Sa3, Sb1, Sb3) coding section 2304 to be performedby (Sa0, Sa2) coding section 2302 and (Sb0, Sb2) coding section 2306.This enables the possibility of being able to decode partial bits Sa0,Sa2, Sb0, and Sb2 without error to be greatly increased, enabling thepossibility of performing erroneous signal point reduction to be greatlyreduced, with the result that bit error rate performances can besignificantly improved.

As modulation signal point arrangements which has 16 signal points, thekind of signal point arrangements shown in FIG. 15A and FIG. 15B aremore suitable for implementation of the kind of error correction codingof this embodiment than 16QAM. This is because, whereas the determinedpartial bits differ according to the area in 16QAM, in the cases shownin FIG. 15A and FIG. 15B the partial bits are fixed at (Sa0, Sa2) and(Sb0, Sb2) irrespective of the area, enabling error correction coding tobe implemented easily. In this embodiment, an example has been describedin which error correction coding is implemented for modulation which has16 signal points, but the same kind of effect as in this embodiment canalso be obtained if the same kind of error correction coding processingas in this embodiment is performed for 64-value M-ary modulation. Inthis case, also, for the same reasons as stated above, use of the kindof signal point arrangements shown in FIG. 18 and FIG. 19 is moresuitable than 64QAM in enabling error correction coding to beimplemented easily.

(Sa1, Sa3, Sb1, Sb3) decoding section 2411 in FIG. 25C haspost-reduction signal point information 515 and 517, and despreadbaseband signals 316 and 306, as input, finds a branch metric byfinding, for example, the squares of the Euclidian distances betweencandidate signal points and baseband signals, finds a path metric fromthe branch metric, and performs decoding, thereby obtaining modulatedsignal A received digital signal 322 and modulated signal B receiveddigital signal 323.

Thus, according to this embodiment, by executing coding processing withpartial bits as a coding unit—that is, coding transmit bits mappedwithin the same signal point set together—in addition to implementationof the configurations in Embodiments 1 through 4, it is possible togreatly improve bit error rate performances on the receiving side inaddition to obtaining the effects of Embodiments 1 through 4.

Also, by executing coding processing with higher error correctioncapability for partial bits than for other bits—that is, coding transmitbits mapped within the same signal point set together—bit error rateperformances on the receiving side can be further improved.

In this embodiment, a case has been described in which thetransmitting-side coding section is configured as shown in FIG. 24, andthe receiving-side signal processing section is configured as shown inFIG. 5, FIG. 25A, FIG. 25B, and FIG. 25C, but the coding section andsignal processing section configurations are not limited to these. FIG.26 shows another example of a coding section configuration, and FIG. 27shows another example of a signal processing section configuration.

In FIG. 26, in which parts corresponding to those in FIG. 24 areassigned the same codes as in FIG. 24, coding section 2500 has an (Sa0,Sa2) coding section 2302, an (Sa1, Sa3) coding section 2501, an (Sb0,Sb2) coding section 2306, and an (Sb1, Sb3) coding section 2503. Codingsections 2302, 2501; 2306, and 2503 have digital signal 1901 as input,and perform coding processing on the respective specific bits.

That is to say, (Sa0, Sa2) coding section 2302 codes bits Sa0 and Sa2contained in digital signal 1901, and outputs bit Sa0 and Sa2 codinginformation 2303; (Sa1, Sa3) coding section 2501 codes bits Sa1 and Sa3contained in digital signal 1901, and outputs bit Sa1 and Sa3 codinginformation 2502; (Sb0, Sb2) coding section 2306 codes bits Sb0 and Sb2contained in digital signal 1901, and outputs bit Sb0 and Sb2 codinginformation 2307; and (Sb1, Sb3) coding section 2503 codes bits Sb1 andSb3 contained in digital signal 1901, and outputs bit Sb1 and Sb3 codinginformation 2504.

(Sa0, Sa1, Sa2, Sa3) signal generation section 2308 has Sa0 and Sa2coding information 2303 and Sa1, Sa3 coding information 2502 as input,generates Sa0, Sa1, Sa2, and Sa3 signals, and outputs these as codeddigital signal 101.

Similarly, (Sb0, Sb1, Sb2, Sb3) signal generation section 2310 has Sb1and Sb3 coding information 2504 and Sb0 and Sb2 coding information 2307as input, generates Sb0, Sb1, Sb2, and Sb3 signals, and outputs these ascoded digital signal 109.

Next, the configuration of signal processing section 2600 in FIG. 27will be described. Signal processing section 2600 in FIG. 27 has asimilar configuration to signal processing section 321 in FIG. 5, exceptthat, as compared with signal processing section 321 in FIG. 5, softdecision decoding sections 2601 and 2602 are provided as partial bitdetermination sections 509 and 512 (that is, partial bit demodulationsection 2610 is composed of separation section 507 and soft decisiondecoding sections 2601 and 2602), and hard decision decoding sections2606 and 2608 are provided. Soft decision decoding section 2601 hasmodulated signal A estimated baseband signal 508 as input, performs softdecision decoding for partial bits Sa0 and Sa2 in FIG. 26, and outputspartial bit Sa0 and Sa2 information thus obtained as modulated signal Adetermined partial bit information 510. Similarly, soft decisiondecoding section 2602 has modulated signal B estimated baseband signal511 as input, performs soft decision decoding for partial bits Sb0 andSb2 in FIG. 26, and outputs partial bit Sb0 and Sb2 information thusobtained as modulated signal B determined partial bit information 513.

Signal point reduction sections 514 and 516 perform candidate signalpoint reduction using determined partial bit information 510 and 513,and send post-reduction signal point information 515 and 517 tolikelihood determination section 2603.

Likelihood determination section 2603 performs likelihood determinationof the candidate signal points with the highest likelihood from thepost-reduction candidate signal points and despread baseband signal1316, and finds bits Sa1, Sa3, Sb1, and Sb3. Then likelihooddetermination section 2603 sends bits Sa1 and Sa3 to hard decisiondecoding section 2606 as bit information 2604, and sends bits Sb1 andSb3 to hard decision decoding section 2608 as bit information 2605.

Hard decision decoding section 2606 obtains modulated signal Apost-error-correction bit information 2607 by performing hard decisiondecoding of bit information 2604. Similarly, hard decision decodingsection 2608 obtains modulated signal B post-error-correction bitinformation 2609 by performing hard decision decoding of bit information2605.

Here, modulated signal A determined partial bit information 510 andmodulated signal A post-error-correction bit information 2607 correspondto final post-error-correction modulated signal A bit information, andmodulated signal B determined partial bit information 513 and modulatedsignal B post-error-correction bit information 2609 correspond to finalpost-error-correction modulated signal B bit information.

Thus, in signal processing section 2600, by providing soft decisiondecoding sections 2601 and 2602, and finding partial bits used in signalpoint reduction by means of soft decision decoding processing, theprobability of error of partial bits can be reduced compared with a casein which hard decision processing is performed, for example, enablingthe final bit error rate performances to be improved. The reason forperforming hard decision processing on signals after likelihooddetermination is that, since determination is carried out for modulatedsignal A and modulated signal B simultaneously when likelihooddetermination is performed, in principal it is difficult to make a softdecision for only modulated signal A or to make a soft decision for onlymodulated signal B.

In this embodiment, a case has been described in which coding isperformed on bits (Sa1, Sa3, Sb1, Sb3) other than the bits for whichpartial bit determination is performed on the receiving side, but it isalso possible for coding not to be performed for bits other than thebits for which partial bit determination is performed. Essentially, thesame kind of effect as in this embodiment can be obtained as long ascoding is performed in partial bit units.

Embodiment 6

In this embodiment, the implementation of trellis coding modulation onthe transmitting side is proposed. A case in which 16QAM is used as themodulation method will be described here by way of example.

The general configuration of a transmitting apparatus is as shown inFIG. 2, and the transmit signal frame configurations are as shown inFIG. 3. The general configuration of a receiving apparatus is as shownin FIG. 4, and the detailed configuration of signal processing section321 in FIG. 4 is as shown in FIG. 5.

In order to implement 16QAM trellis coding modulation, modulationsections 102 and 110 of transmitting apparatus 100 in FIG. 2 can beconfigured as shown in FIG. 28, for example.

In FIG. 28, reference codes 2701, 2702, and 2703 denote shift registersand reference codes 2704 and 2705 denote exclusive OR circuits, and b0,b1, b2, and b3 are generated from inputs a0, a1, and a2. A basebandsignal generation section 2706 has b0, b1, b2, and b3 as input, andobtains a baseband signal 2707 by performing 16QAM mapping.

The operation of a receiving apparatus will now be described. Asdescribed above, the characteristic operation of a receiving apparatusof the present invention lies in partial bit determination sections 509and 512 (FIG. 5). Since similar operations are performed by partial bitdetermination section 509 and partial bit determination section 512, theoperation of partial bit determination section 509 will mainly bedescribed here.

Partial bit determination section 509 has modulated signal A estimatedbaseband signal 508 as input, determines coding related bits—that is,b0, b1, and b2 in FIG. 28—by performing Viterbi decoding, for example,and outputs this information as modulated signal A determined partialbit information 510. Similarly, partial bit determination section 512outputs modulated signal B determined partial bit information 513 (3-bitinformation).

Signal point reduction sections 514 and 516 perform signal pointreduction. Then likelihood detection section 518 determines b3information in FIG. 28 transmitted by modulated signal A, and b3information in FIG. 28 transmitted by modulated signal B, and outputsthis information as a modulated signal A digital signal 519 andmodulated signal B digital signal 520.

Thus, according to this embodiment, performing trellis coding modulationon the transmitting side enables implementation of error correctioncoding to be carried out easily, and bit error rate performances on thereceiving side to be effectively improved with a simple transmittingapparatus configuration.

Embodiment 7

In this embodiment, an actual sample configuration when using 3receiving antennas and 3 transmitting antennas will be described as anexample of a case in which the number of transmitting antennas and thenumber of receiving antennas are greater than two.

Also, in this embodiment, a partial bit determination method and signalpoint reduction method for effectively improving bit error rateperformances are proposed.

FIG. 30, in which parts corresponding to those in FIG. 2 are assignedthe same codes as in FIG. 2, shows the configuration of a transmittingapparatus according to this embodiment. Transmitting apparatus 2900 hasthe same kind of configuration as transmitting apparatus 100 in FIG. 2,except for the fact that it has a transmitting section that transmits amodulated signal C in addition to those transmitting modulated signal Aand modulated signal B. Here, only the configuration of the transmittingsection that transmits modulated signal C will be described.

Modulation section 2902 has a digital signal 2901 and frameconfiguration signal 118 as input, modulates digital signal 2901 inaccordance with frame configuration signal 118, and sends a basebandsignal 2903 thus obtained to a spreading section 2904. Spreading section2904 multiplies baseband signal 2903 by a spreading code, and sends aspread baseband signal 2905 thus obtained to a radio section 2906. Radiosection 2906 executes frequency conversion, amplification, and so forthon spread baseband signal 2905, thereby obtaining a modulated signal2907 (modulated signal C). Modulated signal 2907 is output as a radiowave from an antenna 2908.

Frame configuration signal generation section 117 outputs information onthe frame configurations in FIG. 31, for example, as frame configurationsignal 118.

FIG. 31 shows sample frame configurations of modulated signalstransmitted from antennas 108, 116, and 2908 of transmitting apparatus2900. Modulated signal A transmitted from antenna 108, modulated signalB transmitted from antenna 116, and modulated signal C transmitted fromantenna 2908 have channel estimation symbols 201, 203, and 3001 forchannel estimation, and data symbols 202, 204, and 3002. Transmittingapparatus 2900 transmits modulated signal A, modulated signal B, andmodulated signal C with the frame configurations shown in FIG. 31 atvirtually the same time. Channel estimation symbols 201, 203, and 3001for channel estimation can also be referred to as pilot symbols, uniquewords, or preambles.

FIG. 32, in which parts corresponding to those in FIG. 4 are assignedthe same codes as in FIG. 4, shows the configuration of a receivingapparatus according to this embodiment. Descriptions of parts thatoperate in the same way as in FIG. 4 are omitted from the followingexplanation.

If, in transmitting apparatus 2900 in FIG. 30, a signal transmitted fromantenna 108 is designated Txa(t), a signal transmitted from antenna 116,Txb(t), and a signal transmitted from antenna 2908, Txc (t); and inreceiving apparatus 3100 in FIG. 32, a signal received by antenna 301 isdesignated Rx1(t), a signal received by antenna 311, Rx2(t), and asignal received by antenna 3105, Rx3(t); and, furthermore, propagationfluctuations between the transmitting and receiving antennas aredesignated h11(t) h12(t), h13(t), h21(t), h22(t), h23(t), h31(t),h32(t), and h33(t); then the relational expression in the followingequation holds true, where t denotes time.

$\begin{matrix}{\begin{pmatrix}{{Rx}\; 1(t)} \\{{Rx}\; 2(t)} \\{{Rx}\; 3(t)}\end{pmatrix} = {\begin{pmatrix}{h\; 11(t)} & {h\; 12(t)} & {h\; 13(t)} \\{h\; 21(t)} & {h\; 22(t)} & {h\; 23(t)} \\{h\; 31(t)} & {h\; 32(t)} & {h\; 33(t)}\end{pmatrix}\begin{pmatrix}{{Txa}(t)} \\{{Txb}(t)} \\{{Txc}(t)}\end{pmatrix}}} & (2)\end{matrix}$

A modulated signal C channel fluctuation estimation section 3101 hasdespread baseband signal 306 as input, estimates channel fluctuationusing modulated signal C channel estimation symbol 3001 in the frameconfiguration in FIG. 31, for example, and sends a modulated signal Cchannel fluctuation signal 3102 thus obtained to a signal processingsection 3117. Similarly, a modulated signal C channel fluctuationestimation section 3103 has despread baseband signal 316 as input,estimates channel fluctuation using modulated signal C channelestimation symbol 3001 in the frame configuration in FIG. 31, forexample, and sends a modulated signal C channel fluctuation signal 3104thus obtained to signal processing section 3117.

A radio section 3107 has a received signal 3106 received by antenna 3105as input, executes frequency conversion, quadrature demodulation, and soforth on received signal 3106, and sends a baseband signal 3108 thusobtained to a despreading section 3109. Despreading section 3109despreads baseband signal 3108, and outputs a despread baseband signal3110 thus obtained.

A modulated signal A channel fluctuation estimation section 3111 hasdespread baseband signal 3110 as input, estimates channel fluctuationusing modulated signal A channel estimation symbol 201 in the frameconfiguration in FIG. 31, for example, and sends a modulated signal Achannel fluctuation signal 3112 thus obtained to signal processingsection 3117. Similarly, a modulated signal B channel fluctuationestimation section 3113 has despread baseband signal 3110 as input,estimates channel fluctuation using modulated signal B channelestimation symbol 203 in the frame configuration in FIG. 31, forexample, and sends a modulated signal B channel fluctuation signal 3114thus obtained to signal processing section 3117. In the same way, amodulated signal C channel fluctuation estimation section 3115 hasdespread baseband signal 3110 as input, estimates channel fluctuationusing modulated signal C channel estimation symbol 3001 in the frameconfiguration in FIG. 31, for example, and sends a modulated signal Cchannel fluctuation signal 3116 thus obtained to signal processingsection 3117.

Signal processing section 3117 has despread baseband signals 306, 316,and 3110, modulated signal A channel fluctuation signals 308, 318, and3112, modulated signal B channel fluctuation signals 310, 320, and 3114and modulated signal C channel fluctuation signals 3102, 3104, and 3116,as input, and by performing modulated signal A, B, and C detection,decoding, and so forth, using these signals, obtains a modulated signalA digital signal 322, modulated signal B digital signal 323, andmodulated signal C digital signal 3118.

A sample configuration of signal processing section 3117 is shown inFIG. 33, and another sample configuration of signal processing section3117 is shown in FIG. 34.

First, the configuration in FIG. 33 will be described. In FIG. 33, inwhich parts corresponding to those in FIG. 5 are assigned the same codesas in FIG. 5, separation section 3201 of partial bit demodulationsection 3230 of signal processing section 3117 has modulated signal Achannel fluctuation signals 308, 318, and 3112, modulated signal Bchannel fluctuation signals 310, 320, and 3114, modulated signal Cchannel fluctuation signals 3102, 3104, and 3116, and despread basebandsignals 306, 316, and 3110 as input, and obtains transmit signalsTxa(t), Txb(t), and Txc(t) by performing an inverse matrix computationor MMSE (Minimum Mean Square Error) computation, for example, forEquation (2). Separation section 3201 sends thus obtained modulatedsignal A estimated baseband signal 508 to partial bit determinationsection 509, modulated signal B estimated baseband signal 511 to partialbit determination section 512, and modulated signal C estimated basebandsignal 3207 to partial bit determination section 3208. Partial bitdetermination sections 509, 512, and 3208 send out found partial bitinformation 510, 512, and 3209.

Partial bit determination of partial bit determination sections 509,512, and 3208 can be performed by using the methods in FIG. 9B and FIG.11B above, for example, when the modulation method is 16QAM. In the caseof QPSK, partial bit determination can be implemented by performing thekind of area division shown in FIG. 29, for example. Here, animplementation method in the case of 3 antennas will be described takinga case in which the modulation method is 16QAM, and 2 of 4 bits aredetermined as in FIG. 11B, as an example.

When three 16QAM signals transmitted simultaneously from differentantennas are received, 16×16×16=4096 candidate signal points exist. As 2bits are determined for each of modulated signals A, B, and C by partialbit determination sections 509, 512, and 3208, the 4096 signal pointsare reduced to 4096/4/4/4=64 candidate signal points. Thus, inlikelihood detection section 3212, branch metrics between 64 candidatesignal points and despread baseband signals are found, and by performingnarrowing-down to one candidate signal point and detection, modulatedsignal A, modulated signal B, and modulated signal C digital signals322, 323, and 3213 are obtained.

By thus also performing partial bit determination, reducing the numberof candidate signal points using determined partial bits, and performinglikelihood determination using the reduced candidate signal points evenwhen there are 3 transmitting antennas, 3 receiving antennas, and 3transmit modulated signals, in the same way as when there are 2transmitting antennas, 2 receiving antennas, and 2 transmit modulatedsignals, received digital signals 322, 323, and 3213 of good receptionquality can be obtained with a comparatively small amount ofcomputation.

Next, the configuration in FIG. 34 will be described. Signal processingsection 3117 in FIG. 34, in which parts corresponding to those in FIG.33 are assigned the same codes as in FIG. 33, has a control section3301.

Control section 3301 has modulated signal A channel fluctuation signals308, 318, and 3112, modulated signal B channel fluctuation signals 310,320, and 3114, and modulated signal C channel fluctuation signals 3102,3104, and 3116 as input, and estimates, for example, the received fieldstrength of modulated signal A, the received field strength of modulatedsignal B, and the received field strength of modulated signal C. Controlsection 3301 then outputs control information 3302 such that partial bitdetermination is not performed for only the modulated signal with thelowest field strength.

Assume, for example, that the received field strength of modulatedsignal A is the lowest. In this case, modulated signal A partial bitdetermination section 509 is controlled so as not to perform bitdetermination. That is to say, determined bits are 0 bits. On the otherhand, modulated signal B partial bit determination section 512 andmodulated signal C partial bit determination section 3208 are eachcontrolled so as to perform 2-bit determination. Then signal pointreduction sections 514, 516, and 3210 reduce the 4096 candidate signalpoints to 4096/4/4=256 candidate signal points using 0 modulated signalA determined bits (that is to say, no bits have been determined), 2modulated signal B determined bits, and 2 modulated signal C determinedbits. In likelihood detection section 3212, branch metrics between 256candidate signal points and despread baseband signals are found, and byperforming narrowing-down to one candidate signal point and detection,modulated signal A, modulated signal B, and modulated signal C digitalsignals 322, 323, and 3213 are obtained.

By selecting which modulated signals' partial bits are used for signalpoint reduction in this way, received digital signals with significantlybetter bit error rate performances can be obtained than in a case inwhich partial bits of all modulated signals are simply used for signalpoint reduction (as in the configuration in FIG. 33, for example).

That is to say, when candidate signal point reduction is performedsimply by using the results of partial bit determination for allmodulated signals, the probability of error of partial bit determinationresults for a modulated signal of low reception quality (in the case ofthis embodiment, received field strength) increases, and in line withthis, the probability of not being able to perform candidate signalpoint reduction accurately also increases. As a result, there is a riskof degradation of the bit error rate performances of the final receiveddigital signals. Taking this into consideration, in this embodimentsignal point reduction is performed using only partial bit determinationresults of modulated signals that have good reception quality.

Thus, according to this embodiment, by providing a control section 3301that controls which modulated signals' partial bits are used forcandidate signal point reduction by signal point reduction sections 514,516, and 3210 based on the reception quality of each modulated signal,received digital signals 322, 323, and 3213 with significantly betterbit error rate performances can be obtained.

In this embodiment, a case in which received field strength is used as areception quality parameter has been described as an example, but thisis not a limitation, and it is also possible, for example, to find thecarrier power to noise power ratio of each modulated signal afterinverse matrix computation or MMSE computation, and use this as areception quality parameter for each modulated signal.

Also, in this embodiment an example has been described in which partialbits are determined for only two modulated signals, but the presentinvention can be similarly implemented by determining partial bits foronly one modulated signal.

Furthermore, the number of bits determined as partial bits may be variedaccording to a reception quality priority order. For example,compatibility between good bit error rate performances and a smallcomputation scale can be achieved by having 2 bits determined by themodulated signal A partial bit determination section, 1 bit determinedby the modulated signal B partial bit determination section, and 0 bitsdetermined by the modulated signal C partial bit determination sectionwhen the relationship “modulated signal A received fieldstrength>modulated signal B received field strength>modulated signal Creceived field strength” holds true.

That is to say, if the number of partial bits used in each modulatedsignal is controlled by control section 3301 in signal point reductionby signal point reduction sections 514, 516, and 3210 based on thereception quality of each modulated signal, received digital signals322, 323, and 3213 with significantly better bit error rate performancescan be obtained.

In this embodiment, a case has been described in which 16QAM is used asthe modulation method, but the same kind of effect can also be obtainedwhen a different modulation method is used.

Also, in this embodiment, a case in which the number of transmittingantennas is 3, the number of receiving antennas is 3, and the number oftransmit modulated signals is 3 has been described as an example, butthis embodiment can be widely applied to cases with n transmittingantennas, n receiving antennas, and n transmit signals (where n>2). Forexample, in a case in which the number of transmitting antennas is 2,the number of receiving antennas is 2, and the number of transmitmodulated signals is 2, if modulated signal A received fieldstrength>modulated signal B received field strength, determination maybe carried out whereby 2 partial bits are determined for modulatedsignal A, 1-bit or 0-bit partial determination is performed formodulated signal B, and the remaining bits are then included byperforming likelihood determination.

Furthermore, in this embodiment, a case in which coding is not performedhas been described as an example, but the same kind of effect can alsobe obtained by using the determination method of this embodiment whenerror correction coding is applied.

A method may also be used whereby modulated signal A, modulated signalB, and modulated signal C received digital signals 322, 323, and 3213are obtained by determining partial bits of modulated signal A andmodulated signal B and obtaining branch metric BM_(AB) from candidatesignal points reduced using these partial bits, determining partial bitsof modulated signal A and modulated signal C and obtaining branch metricBM_(AC) from candidate signal points reduced using these partial bits,and determining partial bits of modulated signal B and modulated signalC and obtaining branch metric BM_(BC) from candidate signal pointsreduced using these partial bits, and performing determination usingthese branch metrics BM_(AB), BM_(AC), and BM_(BC).

As a result of performing a simulation, it was found that the methoddescribed in this embodiment, whereby partial bits used in candidatesignal point reduction by signal point reduction sections are controlledaccording to the reception quality of each modulated signal, enablesreceived digital signals 322, 323, and 3213 with extremely good biterror rate performances to be obtained especially when MMSE is performedby separation section 3201 (FIG. 34).

Embodiment 8

In above Embodiment 1, a 1-bit partial determination method when themodulation method is 16QAM (FIG. 9B) was described, but in thisembodiment, a 1-bit partial determination method will be described thatenables significantly better bit error rate performances to be obtained.

FIG. 35 shows an example of 16QAM signal point arrangement and areceived-signal signal point. In this figure, reference codes 3401through 3416 denote 16QAM signal points (candidate signal points), andreference code 3417 denotes a received-signal signal point (receptionpoint). FIG. 35 also shows the relationships of the 4 bits (S0, S1, S2,S3) of signal points 3401 through 3416.

In a 1-bit partial bit determination method of this embodiment, first,the Euclidian distances between received-signal signal point 3417 and 16QAM signal points 3401 through 3416 are found, the 16QAM signal pointwith the minimum Euclidian distance is found, and the 4 bits indicatedby that signal point are found. In the example in FIG. 35, signal point3407 is detected as the signal point having the minimum Euclidiandistance from reception point 3417, and (S0, S1, S2, S3)=(1, 1, 1, 1) isfound as the 4-bit bit string indicated by that signal point 3407.

Next, the following Euclidian distances are found for the 4 bits (S0,S1, S2, S3).

As “1” has been found for bit S0, signal points with “0” in the S0position of bit string (S0, S1, S2, S3) are searched for. As a result ofthe search, signal points 3401, 3402, 3405, 3406, 3409, 3410, 3413, and3414 are obtained. Then the minimum Euclidian distance between these 8signal points and reception point 3417 is found, and the value ofminimum Euclidian distance D_(min,S0) is found.

Similarly, as “1” has been found for bit S1, signal points with “0” inthe S1 position of bit string (S0, S1, S2, S3) are searched for. As aresult of the search, signal points 3401, 3404, 3405, 3408, 3409, 3412,3413, and 3416 are obtained. Then the minimum Euclidian distance betweenthese 8 signal points and reception point 3417 is found, and the valueof minimum Euclidian distance D_(min,S1) is found.

Similarly, as “1” has been found for bit S2, signal points with “0” inthe S2 position of bit string (S0, S1, S2, S3) are searched for. As aresult of the search, signal points 3409, 3410, 3411, 3412, 3413, 3414,3415, and 3416 are obtained. Then the minimum Euclidian distance betweenthese 8 signal points and reception point 3417 is found, and the valueof minimum Euclidian distance D_(min,S2) is found.

Similarly, as “1” has been found for bit S3, signal points with “0” inthe S3 position of bit string (S0, S1, S2, S3) are searched for. As aresult of the search, signal points 3401, 3402, 3403, 3404, 3413, 3414,3415, and 3416 are obtained. Then the minimum Euclidian distance betweenthese 8 signal points and reception point 3417 is found, and the valueof minimum Euclidian distance D_(min,S3) is found.

That is to say, signal points that have a value that is the NOT ofdetermined bit Sx are searched for, the minimum Euclidian distancebetween these signal points and reception point 3407 is found, and thevalue of minimum Euclidian distance D_(min,Sx) is found.

Then the item with the maximum value among Dmin,S0, D_(min,s1),D_(min,S2), and D_(min,S3) is searched for. If, for example, the itemwith the maximum value is D_(mins,S0) is determined. That is to say,when the item with the maximum value is D_(min,Sy), Sy is determined. Bythis means, the most probable bit within bit string (S0, S1, S2, S3) canbe chosen. The above-described processing is summarized in FIG. 36.

First, when processing is started in step ST0, candidate signal point3407 having the minimum Euclidian distance from reception point 3417 isdetected in step ST1.

In step ST2, the bits contained in bit string (1, 1, 1, 1) correspondingto candidate signal point 3407 are inverted one bit at a time. In stepST3, for each inverted bit, a plurality of candidate signal pointscontaining the inverted bit are searched for. In step ST4, for eachinverted bit, the minimum Euclidian distance between a reception pointand the plurality of candidate signal points found in step ST3 isdetected. In step ST5, the maximum Euclidian distance is detected fromamong the minimum Euclidian distances of each inverted bit detected instep ST4. In step ST6, the bit corresponding to the maximum Euclidiandistance detected in step ST5 is taken as the bit with the highestreliability within bit string (1, 1, 1, 1) represented by candidatesignal point 3407 detected in step ST1, and this is adopted as a partialbit.

That is to say, in step ST2 through step ST6, the bit with the highestreliability within a bit string represented by a candidate signal pointdetected in step ST1 is determined. Then processing ends in step ST7.

Thus, according to this embodiment, 1 bit with an extremely lowprobability of being erroneous can be determined by: detecting acandidate signal point for which the Euclidian distance from a modulatedsignal reception point is a minimum; inverting the bits contained in thebit string corresponding to the detected candidate signal point one at atime; searching, for each inverted bit, for a plurality of candidatesignal points containing the inverted bit; detecting, for each invertedbit, the minimum Euclidian distance between the reception point and theaforementioned found plurality of candidate signal points; detecting themaximum Euclidian distance among the minimum Euclidian distances of eachaforementioned inverted bit; and determining the bit corresponding tothe detected maximum Euclidian distance to be a partial bit.

If this kind of 1-bit determination algorithm is here executed bypartial bit determination sections 509 and 512, a partial bit (1 bit)with an extremely low probability of being erroneous can be determined,enabling the bit error rate performance of a finally obtained receiveddigital signal to be improved. The 1-bit determination algorithm of thisembodiment is not limited to a case in which a receiving apparatus witha configuration described in an above embodiment is used, and can bewidely applied to cases in which it is wished to select the bit with thelowest probability of being erroneous within a bit string represented bya signal point.

In this embodiment, 16QAM has been described as an example, but 1 bitcan also be similarly determined when a different modulation method isused. Also, this embodiment can be similarly implemented when thesquares of Euclidian distances are found instead of Euclidian distances.

Embodiment 9

In above Embodiment 3, a 64QAM partial bit determination method wasdescribed. In this embodiment, a 2-bit partial bit determination methodand 4-bit partial bit determination method different from thosedescribed in Embodiment 3 will be described. Referring to FIG. 5, forexample, the partial bit determination described below is performed bymeans of partial bit determination sections 509 and 512 with modulatedsignal A and B estimated baseband signals 508 and 511 separated byseparation section 507 as reception points.

(i) 2-Bit Partial Bit Determination

FIG. 37 shows the relationship between signal points (candidate signalpoints) in a 64QAM in-phase I-orthogonal Q plane and the 6 bitstransmitted at each signal point, and also the relationship of 2 partialbits determined by reception signal point presence positions.Specifically, in FIG. 37, the 6 bits shown under each signal point arethe 6 bits corresponding to each signal point, and reference codes 3601and 3602 denote reception points.

In this embodiment, the IQ plane is divided into 9 areas as indicated bythe dotted lines. This area division method is a characteristic of thisembodiment. With the area division shown in FIG. 37, the number of bitsfor which the determined bits are always the same in each area is two.That is to say, if six 64QAM bits are designated “first bit, second bit,third bit, fourth bit, fifth bit, sixth bit” sequentially from left toright, in the area in which reception point 3601 is present, third bit“0” and sixth bit “1” are the same for all signal points (candidatesignal points). Also, in the area in which reception point 3602 ispresent, third bit “1” and fourth bit “1” are the same for all signalpoints (candidate signal points).

In this embodiment, which bits of the 6 bits making up one symbol aredemodulated as partial bits is changed according to which of the dividedareas a reception point is present in, while performing this kind ofarea division. In other words, when the IQ plane is divided into aplurality of areas, only bits with the same determined value in an areaare demodulated as partial bits. By this means, partial bit error rateperformance can be improved.

Specifically, when a reception point is present at the positionindicated by reference code 3601, “xx0xx1” is determined. That is tosay, only third bit “0” and sixth bit “1” are determined to be(demodulated as) partial bits. Here, x indicates indetermination (thatis, a bit whose value is not determined).

Similarly, when a reception point is present at the position indicatedby reference code 3602, “xx11xx” is determined. That is to say, onlythird bit “1” and fourth bit “1” are determined to be (demodulated as)partial bits. Incidentally, if a reception point is present on aboundary between areas, it may be assigned to either area.

Then candidate signal point reduction is performed based on thedetermined 2 partial bits, and the indeterminate 4 bits are found bylikelihood detection.

(ii) 4-Bit Partial Bit Determination

Next, the 4-bit partial bit determination method will be explained usingFIG. 38.

FIG. 38 shows the relationship between signal points (candidate signalpoints) in a 64QAM in-phase I-orthogonal Q plane and the 6 bitstransmitted at each signal point, and also the relationship of 4 partialbits determined by reception signal point presence positions.Specifically, in FIG. 38, the 6 bits shown under each signal point arethe 6 bits corresponding to each signal point, and reference codes 3701and 3702 denote reception points.

In this embodiment, the IQ plane is divided into 49 areas as indicatedby the dotted lines. With the area division shown in FIG. 38, the numberof bits for which the determined bits are always the same in each areais four. That is to say, if six 64QAM bits are designated “first bit,second bit, third bit, fourth bit, fifth bit, sixth bit” sequentiallyfrom left to right, in the area in which reception point 3701 ispresent, second bit “1”, third bit “0”, fifth bit “1”, and sixth bit “0”are the same for all signal points (candidate signal points). Also, inthe area in which reception point 3702 is present, second bit “1”, thirdbit “1”, fourth bit “1”, and sixth bit “0” are the same for all signalpoints (candidate signal points).

Here, when a reception point is present at the position indicated byreference code 3701, for example, “x10x10” is determined. That is tosay, only second bit “1”, third bit “0”, fifth bit “1”, and sixth bit“0” are determined to be (demodulated as) partial bits.

Similarly, when a reception point is present at the position indicatedby reference code 3702, “x111x0” is determined. That is to say, onlysecond bit “1”, third bit “1”, fourth bit “1”, and sixth bit “0” aredetermined to be (demodulated as) partial bits.

Then candidate signal point reduction is performed based on thedetermined 4 partial bits, and the indeterminate 2 bits are found bylikelihood detection.

Thus, according to this embodiment, in demodulating some bits of amodulated signal that has undergone 64QAM modulation, which bits in a6-bit bit string making up one symbol are demodulated as partial bits ischanged according to which area on the IQ plane a relevant receptionsignal point is present in, thereby improving the bit error performanceof partial bits determined by partial bit determination sections 509 and512, and so improving the reliability of reduced candidate signal pointsused by likelihood detection section 518. As a result, the bit errorperformance of final received digital signals 322 and 323 can beimproved.

That is to say, if the kind of 2-bit or 4-bit determination algorithmdescribed in this embodiment is executed by partial bit determinationsections 509 and 512, partial bits (2 bits or 4 bits) with an extremelylow probability of being erroneous can be determined, enabling the biterror rate performance of finally obtained received digital signals 322and 323 to be improved. The 16QAM 2-bit or 4-bit determination algorithmof this embodiment is not limited to a case in which a receivingapparatus with a configuration described in an above embodiment is used,and can be widely applied to cases in which it is wished to select bitswith the lowest probability of being erroneous within a bit stringrepresented by a signal point.

In this embodiment, a method of determining 2 bits as partial bits and amethod of determining 4 bits as partial bits have been described, and itis appropriate to select either of these methods based on the receivedfield strength of a received signal. For example, 4-bit determinationmay be used when the received field strength of modulated signal A isgreater than or equal to a predetermined threshold value, and 2-bitdetermination when the received field strength of modulated signal A isless than the threshold value. This enables the amount of computation tobe reduced without lowering bit error rate performance.

Embodiment 10

In Embodiment 7, a method was described whereby the number of partialbit determinations is controlled according to the received fieldstrength of each modulated signal. In this embodiment, a detaileddescription is given of the configuration of a receiving apparatus thatfinds the signal to noise ratio of each modulation method after inversematrix computation or MMSE computation, and uses these to determine thenumber of partial bit determinations.

Assume that the vector (Rx1(t), Rx2(t), Rx3(t))T in Equation (2) isdesignated rx, the matrix in Equation (2) is designated H, and thevector (Txa(t), Txb(t), Txc(t)) T is designated tx. Then, if an addednoise vector is designated n, Equation (2) can be represented as shownin the following equation.

rx=Htx+n  (3)

When inverse matrix computation is performed, if the inverse matrix of His represented by H⁻¹, the following equation is obtained from Equation(3).

tx′=tx+H ⁻¹ n  (4)

where tx′ is a receiving apparatus estimate for tx.

Here, the signal to noise ratio after inverse matrix computation can beobtained by finding the ratio of tx signal power to noise power foundfrom H⁻¹n. This is a known technology described, for example, in “ASDM-COFDM scheme employing a simple feed-forward inter-channelinterference canceller for MIMO based broadband wireless LANs” IEICETransaction on Communications, vol.E86-B, no. 1, pp. 283-290, January2003, and therefore a detailed description thereof is omitted here.

Specifically, to give a description applicable to signal processingsection 3117 in FIG. 34, for example, control section 3301 has modulatedsignal A channel fluctuation signals 308, 318, and 3112, modulatedsignal B channel fluctuation signals 310, 320, and 3114, and modulatedsignal C channel fluctuation signals 3102, 3104, and 3116 as input,finds H⁻¹ in Equation (4), and obtains the modulated signal A signal tonoise ratio after inverse matrix computation, modulated signal B signalto noise ratio after inverse matrix computation, and modulated signal Csignal to noise ratio after inverse matrix computation.

When the modulation method of modulated signals A, B, and C is 64QAM,and, for example, the relationship “modulated signal A signal to noiseratio after inverse matrix computation>modulated signal B signal tonoise ratio after inverse matrix computation>modulated signal C signalto noise ratio after inverse matrix computation” holds true, and if thenumber of bits determined by modulated signal A partial bitdetermination section 509 is designated ma bits, the number of bitsdetermined by modulated signal B partial bit determination section 512is designated mb bits, and the number of bits determined by modulatedsignal C partial bit determination section 3208 is designated mc bits,control section 3301 controls the number of bits that are determined sothat the relationship ma>mb>mc holds true. That is to say, more partialbit determinations are performed for a modulated signal with a largersignal to noise ratio after inverse matrix computation.

By controlling how many partial bits of each modulated signal are usedin signal point reduction by signal point reduction sections 514, 516,and 3210 by means of control section 3301 based on the signal to noiseratio after inverse matrix computation of each modulated signal in thisway, received digital signals 322, 323, and 3213 with significantlybetter bit error rate performance can be obtained.

When the modulation method is 64QAM, if the partial bit determinationmethods of Embodiment 8 or Embodiment 9 are used, 1 bit, 2 bits, and 4bits are possible as number of bits ma, mb, and mc determined by thepartial bit determination sections. However, this embodiment is notlimited to this case, and if the signal to noise ratio after inversematrix computation of a modulated signal is extremely large, all bits(that is, 6 bits) may be determined as partial bits.

A case has been described above in which the number of bits determinedas partial bits is decided based on the signal to noise ratio afterinverse matrix computation. Next, a case will be described in which thenumber of bits determined as partial bits is decided based on the signalto noise ratio after MMSE computation, which is a linear transformationsimilar to an inverse matrix computation.

Matrix G of the following equation is calculated based on matrix H inEquation (3).

G=H ^(H)(HH ^(H)+σ² I)⁻¹  (5)

where H^(H) is the complex conjugate transpose of H, and I is a unitmatrix.

An estimated signal of each modulated signal can be obtained bymultiplying Equation (3) by the matrix of Equation (5). Then the signalto noise ratio of an estimated signal obtained by multiplying Equation(3) by the matrix of Equation (5) is calculated. Such signal to noiseratio calculation is a known technology described, for example, in“Performance improvement of ordered successive detection with imperfectchannel estimates for MIMO systems” IEICE Transaction on Communications,vol.E86-B, no. 11, pp. 3200-3208, November 2003, and therefore adetailed description thereof is omitted here.

Specifically, to give a description applicable to signal processingsection 3117 in FIG. 34, for example, control section 3301 has modulatedsignal A channel fluctuation signals 308, 318, and 3112, modulatedsignal B channel fluctuation signals 310, 320, and 3114, and modulatedsignal C channel fluctuation signals 3102, 3104, and 3116 as input,finds matrix G of Equation (5), multiplies Equation (3) by matrix G ofEquation (5), and obtains the modulated signal A signal to noise ratioafter MMSE computation, modulated signal B signal to noise ratio afterMMSE computation, and modulated signal C signal to noise ratio afterMMSE computation.

When the modulation method of modulated signals A, B, and C is 64QAM,and, for example, the relationship “modulated signal A signal to noiseratio after MMSE computation>modulated signal B signal to noise ratioafter MMSE computation>modulated signal C signal to noise ratio afterMMSE computation” holds true, and if the number of bits determined bymodulated signal A partial bit determination section 509 is designatedma bits, the number of bits determined by modulated signal B partial bitdetermination section 512 is designated mb bits, and the number of bitsdetermined by modulated signal C partial bit determination section 3208is designated mc bits, control section 3301 controls the number of bitsthat are determined so that the relationship ma>mb>mc holds true. Thatis to say, more partial bit determinations are performed for a modulatedsignal with a larger signal to noise ratio after MMSE computation.

By controlling how many partial bits of each modulated signal are usedin signal point reduction by signal point reduction sections 514, 516,and 3210 by means of control section 3301 based on the signal to noiseratio after MMSE computation of each modulated signal in this way,received digital signals 322, 323, and 3213 with significantly betterbit error rate performance can be obtained.

In this embodiment, a case in which the number of transmitting antennasis 3, the number of receiving antennas is 3, and the number of transmitmodulated signals is 3 has been described as an example, but thisembodiment can be widely applied to cases with n transmitting antennas,n receiving antennas, and n transmit signals (where n>2). For example,in a case in which the number of transmitting antennas is 2, the numberof receiving antennas is 2, and the number of transmit modulated signalsis 2, if modulated signal A signal to noise ratio after inverse matrixcomputation or MMSE computation>modulated signal B signal to noise ratioafter inverse matrix computation or MMSE computation, determination maybe carried out whereby, for example, 4 partial bits are determined formodulated signal A, 2-bit or 1-bit partial determination is performedfor modulated signal B, and the remaining bits are then included byperforming likelihood determination.

As a result of performing a simulation, it was found that the methoddescribed in this embodiment, whereby partial bits used in candidatesignal point reduction by signal point reduction sections are controlledaccording to the signal to noise ratio of a modulated signal afterinverse matrix computation or MMSE computation, enables received digitalsignals 322, 323, and 3213 with extremely good bit error rateperformances to be obtained especially when MMSE is performed byseparation section 3201 (FIG. 34).

Embodiment 11

Generally, it is necessary for likelihood detection section 518 (3212)to find the square of the Euclidian distance between a candidate signalpoint and reception point, and the computational complexity (inparticular, the number of multipliers) increases as the number ofantennas and modulation M-ary number increase. The present inventionreduces the computational complexity while suppressing degradation ofbit error rate performance, and in this embodiment a method is proposedwhereby the computational complexity of likelihood detection section 518(3212) is significantly reduced compared with the above-describedembodiments.

In this embodiment, calculation of Euclidian distance squares bylikelihood detection section 518 (3212) is replaced by calculation forapproximation by means of Manhattan distances. By this means, likelihooddetection is performed by likelihood detection section 518 (3212)without using multipliers.

This calculation method will be explained using FIG. 39. The square ofthe Euclidian distance between a candidate signal point and receptionsignal point in the in-phase I-orthogonal Q plane, x²+y², is the valuethat it is actually wished to find. However, a method of approximatingthis as |x|+|y| is the Manhattan distance method. In the followingdescription, |x|<|y| will be taken as an example, but when |x|>|y|, asimilar procedure as for |x|<|y| can be assumed, with x and y switchedaround.

When a reception signal point is at position <1>, the Euclidian distanceis 1.414×, whereas the Manhattan distance is 2×. Also, when a receptionsignal point is at position <2>, the Euclidian distance is x and theManhattan distance is also x—that is, the Euclidian distance andManhattan distance are equal. As can be seen from the above, as |x|increases (where |x|<|y|), the Manhattan distance approximation errorrelative to the Euclidian distance increases.

In this embodiment, a method of solving this problem is proposed. Atthis time, in this embodiment, the provision of multipliers is avoided,and an increase in computational complexity is prevented by using aconfiguration that employs a bit shift, adder, and comparator.

FIG. 40 shows the size relationship of |x| and |y|, and approximationsof Euclidian distances found by likelihood detection according to thisembodiment. Specifically, likelihood detection is performed with theEuclidian distance approximation taken to be |y| when the sizerelationship of I-direction distance x and Q-direction distance y for acandidate signal point and reception signal point on the IQ plane is0≦|x|≦|y|×(1+1/4+1/8), with the Euclidian distance approximation takento be |y|×(1+1/8) when that size relationship is|y|×(1+1/4+1/8)≦|x|≦|y|×(1+1/2+1/8), with the Euclidian distanceapproximation taken to be |y|×(1+1/4) when that size relationship is|y|×(1+1/2+1/8)≦|x|≦|y|×(1+1/2+1/4+1/8), and with the Euclidian distanceapproximation taken to be |y|×(1+1/4+1/8) when that size relationship is|y|×(1+1/2+1/4+1/8)≦|x|≦|y|. It is here assumed that |x|<|y|.

At this time, the multiplication factors are factors obtained byaddition of any of 1, ½, ¼, or ⅛, and therefore all calculations can becomposed of bit shifts and additions. Therefore, multipliers are notnecessary, and an increase in computational complexity can be avoided.If |x|>|y|, FIG. 40 should be read with x and y switched around.

FIG. 41 shows a sample circuit configuration for implementing anapproximation method by means of Manhattan distance as shown in FIG. 40.An |x| and |y| calculation section 4003 has candidate signal pointinformation 4001 and reception signal point information 4002 as input,finds |x| and |y| in the in-phase I-orthogonal Q plane, and outputsthese as 4004 and 4005. It is here assumed that |x|<|y|. That is to say,the larger of the two obtained absolute values is taken to be |y|, andthe smaller is taken to be |x|.

A calculation section 4006 has |x| and |y| as input, finds a sizerelationship such as on the left side of FIG. 40 by means of acomparison operation, determines which of the four right-sideexpressions in FIG. 40 is to be used as a Euclidian distanceapproximation according to the comparison result, and finds a Euclidiandistance approximation 4007 using the determined expression. Calculationsection 4006 may, for example, be provided with a comparison operationsection that performs the left-side comparison operations in FIG. 40 andfour operation sections that implement the right-side expressions inFIG. 40, and may find Euclidian distance approximation 4007 by selectingan operation section corresponding to a comparison result among the fouroperation sections based on a comparison result obtained by thecomparison operation section. In this case, the comparison operationsection can perform operations for all the operation sections by meansof bit shifts and additions only, enabling the kind of calculations inFIG. 40 to be implemented with low computational complexity.

By performing the above kind of approximation, a Euclidian distanceapproximation can be implemented with good precision and with lowcomputational complexity.

With this embodiment, a case has been described in which a Euclidiandistance approximation is found based on a correspondence table betweensize relationships and Euclidian approximations as shown in FIG. 40, butthis embodiment is not limited to this approach, the essential pointbeing that, as long as a Euclidian distance approximation is found bymeans of circuitry comprising a bit shift, adder, and comparator, aEuclidian distance approximation can be achieved with good precision andwith low computational complexity.

Embodiment 12

Up to now, 1-bit and 2-bit partial bit determination methods have beendescribed as 16QAM partial bit determination methods. In thisembodiment, a 16QAM 3-bit partial bit determination method not hithertodealt with will be described in detail.

FIG. 35 shows an example of 16QAM signal point arrangement and areceived-signal signal point. In this figure, reference codes 3401through 3416 denote 16QAM signal points (candidate signal points), andreference code 3417 denotes a received-signal signal point (receptionpoint). FIG. 35 also shows the relationships of the 4 bits (S0, S1, S2,S3) of signal points 3401 through 3416.

In a 3-bit partial bit determination method of this embodiment, first,the Euclidian distances between received-signal signal point 3417 and16QAM signal points 3401 through 3416 are found, the 16QAM signal pointwith the minimum Euclidian distance is found, and the 4 bits indicatedby that signal point are found. In the example in FIG. 35, signal point3407 is detected as the signal point having the minimum Euclidiandistance from reception point 3417, and (S0, S1, S2, S3)=(1, 1, 1, 1) isfound as the 4-bit bit string indicated by that signal point 3407.

Next, the following Euclidian distances are found for the 4 bits (S0,S1, S2, S3).

As “1” has been found for bit S0, signal points with “0” in the S0position of bit string (S0, S1, S2, S3) are searched for. As a result ofthe search, signal points 3401, 3402, 3405, 3406, 3409, 3410, 3413, and3414 are obtained. Then the minimum Euclidian distance between these 8signal points and reception point 3417 is found, and the value ofminimum Euclidian distance D_(min,S0) is found.

Similarly, as “1” has been found for bit S1, signal points with “0” inthe S1 position of bit string (S0, S1, S2, S3) are searched for. As aresult of the search, signal points 3401, 3404, 3405, 3408, 3409, 3412,3413, and 3416 are obtained. Then the minimum Euclidian distance betweenthese 8 signal points and reception point 3417 is found, and the valueof minimum Euclidian distance D_(min,S1) is found.

Similarly, as “1” has been found for bit S2, signal points with “0” inthe S2 position of bit string (S0, S1, S2, S3) are searched for. As aresult of the search, signal points 3409, 3410, 3411, 3412, 3413, 3414,3415, and 3416 are obtained. Then the minimum Euclidian distance betweenthese 8 signal points and reception point 3417 is found, and the valueof minimum Euclidian distance D_(min,S2) is found.

Similarly, as “1” has been found for bit S3, signal points with “0” inthe S3 position of bit string (S0, S1, S2, S3) are searched for. As aresult of the search, signal points 3401, 3402, 3403, 3404, 3413, 3414,3415, and 3416 are obtained. Then the minimum Euclidian distance betweenthese 8 signal points and reception point 3417 is found, and the valueof minimum Euclidian distance D_(min,S3) is found.

That is to say, signal points that have a value that is the NOT ofdetermined bit Sx are searched for, the minimum Euclidian distancebetween these signal points and reception point 3407 is found, and thevalue of minimum Euclidian distance D_(min,Sx) is found.

Then the item with the minimum value among D_(min,S0), D_(min,S1),D_(min,S2), and D_(min,S3) is searched for. If, for example, the itemwith the minimum value is D_(min,S0), the three bits other than S0—thatis, is S1, S2, and S3—are determined. That is to say, when the item withthe minimum value is D_(min,Sy), the bits other than Sy are determined.By this means, the most probable 3 bits within bit string (S0, S1, S2,S3) can be chosen.

The above-described processing is summarized in FIG. 42.

First, when processing is started in step ST0, candidate signal point3407 having the minimum Euclidian distance from reception point 3417 isdetected in step ST1.

In step ST2, the bits contained in bit string (1, 1, 1, 1) correspondingto candidate signal point 3407 are inverted one bit at a time. In stepST3, for each inverted bit, a plurality of candidate signal pointscontaining the inverted bit are searched for. In step ST4, for eachinverted bit, the minimum Euclidian distance between a reception pointand the plurality of candidate signal points found in step ST3 isdetected. In step ST5, the minimum Euclidian distance is detected fromamong the minimum Euclidian distances of each inverted bit detected instep ST4. In step ST6, the bit corresponding to the minimum Euclidiandistance detected in step ST5 is taken to be the bit with the lowestreliability within bit string (1, 1, 1, 1) represented by candidatesignal point 3407 detected in step ST1, and the value of bits other thanthis bit is determined.

That is to say, in step ST2 through step ST6, bits other than the bitwith the lowest reliability within a bit string represented by acandidate signal point detected in step ST1 are determined. Thenprocessing ends in step ST7.

Thus, according to this embodiment, 3 bits with an extremely lowprobability of being erroneous can be determined by: detecting acandidate signal point for which the Euclidian distance from a modulatedsignal reception point is a minimum; inverting the bits contained in thebit string corresponding to the detected candidate signal point one at atime; searching, for each inverted bit, for a plurality of candidatesignal points containing the inverted bit; detecting, for each invertedbit, the minimum Euclidian distance between the reception point and theaforementioned found plurality of candidate signal points; detecting theminimum Euclidian distance among the minimum Euclidian distances of eachaforementioned inverted bit; and determining bits other than the bitcorresponding to the detected minimum Euclidian distance to be partialbits.

If this kind of 3-bit determination algorithm is here executed bypartial bit determination sections 509 and 512, partial bits (3 bits)with an extremely low probability of being erroneous can be determined,enabling the bit error rate performance of a finally obtained receiveddigital signal to be improved. The 3-bit determination algorithm of thisembodiment is not limited to a case in which a receiving apparatus witha configuration described in an above embodiment is used, and can bewidely applied to cases in which it is wished to select bits with a lowprobability of being erroneous within a bit string represented by asignal point.

In this embodiment, 16QAM has been described as an example, but bitsexcluding 1 bit can also be determined by means of the same kind ofalgorithm when a different modulation method is used. Also, thisembodiment can be similarly implemented when the squares of Euclidiandistances are found instead of Euclidian distances.

Embodiment 13

Up to now, a number of partial bit determination methods have beendescribed for demodulating a plurality of modulated signals transmittedfrom a plurality of antennas with comparatively low computationalcomplexity and with good bit error rate performance. This embodimentproposes the application of an above-described kind of partial bitdetermination method to MLD using QR decomposition, as shown, forexample, in “Likelihood function for QR-MLD suitable for soft-decisionturbo decoding and its performance for OFDM MIMO multiplexing inmultipath fading channels,” IEICE Transaction on Communicationsvol.E88-B, no. 1, pp. 47-57, 2005.

FIG. 43 shows a sample configuration of this embodiment. In thisembodiment, a case in which there are 3 transmitting antennas and 3receiving antennas, and the modulation method is 16QAM, is described asan example. Signal processing section 4200 in FIG. 43 is used as signalprocessing section 3117 in FIG. 32.

Three modulated signals are transmitted from a transmitting apparatus,and when the receiving apparatus receives these modulated signals atthree antennas, the relationship in Equation (2) holds true as describedabove. Here, the matrix in Equation (2) is assumed to be represented byH.

A QR decomposition section 4201 obtains triangular matrix R representedby the following equation by performing QR decomposition using unitarymatrix Q.

$\begin{matrix}\begin{matrix}{R = {Q\; H}} \\{= \begin{pmatrix}r_{11} & r_{12} & r_{13} \\0 & r_{22} & r_{23} \\0 & 0 & r_{33}\end{pmatrix}}\end{matrix} & (6)\end{matrix}$

Then QR decomposition section 4201 obtains the following relationalexpression by multiplying matrix Q complex conjugate transpose matrixQ^(H) by the Equation (2) received signals.

$\begin{matrix}{\begin{pmatrix}Z_{1} \\Z_{2} \\Z_{3}\end{pmatrix} = {{Q^{H}{Rx}} = {R\begin{pmatrix}{Tx}_{a} \\{Tx}_{b} \\{Tx}_{c}\end{pmatrix}}}} & (7)\end{matrix}$

QR decomposition section 4201 then outputs signal Z₁ (4202) obtained bymeans of Equation (7) to a candidate signal point computation section4214, signal Z₂ (4203) to a candidate signal point computation section4210, and signal Z₃ (4204) to a partial bit determination section 4208,and also outputs row 1 of matrix R (4205) obtained by means of Equation(6) to candidate signal point computation section 4214, row 2 of matrixR (4206) to candidate signal point computation section 4210, and row 3of matrix R (4207) to partial bit determination section 4208.

Partial bit determination section 4208 has signal Z₃ (4204) and row 3 ofmatrix R (4207) as input. Here, signal Z₃ contains only a modulatedsignal Tx_(c) component. Therefore, 16QAM partial bit determinationmethods described hitherto can be used by performing channel fluctuationcorrection. Thus partial bit determination section 4208 outputs ahigh-likelihood partial bit 4209 of modulated signal Tx_(c) byperforming the same kind of partial bit determination as in the aboveembodiments.

Candidate signal point computation section 4210 has signal Z₂ (4203),row 2 of matrix R (4206), and modulated signal Tx_(c) high-likelihoodpartial bit 4209 as input. Candidate signal point computation section4210 performs signal point reduction using these, and outputs acandidate signal point signal 4211. This will now be explained indetail. Signal Z₂ is composed of only modulated signal TX_(b) andmodulated signal Tx_(c) components. Therefore, when the signals are16QAM signals, for example, there are 16 (modulated signal b)×16(modulated signal c)=256 candidate signal points, but for modulatedsignal c, candidate signal point computation section 4210 computescandidate signal point signal 4211 using only 2 bits determined ashaving high likelihood by partial bit determination section 4208. Thatis to say, the number of candidate signal points computed by candidatesignal point computation section 4210 is 16 (modulated signal b)×4(modulated signal c)=64. In this way, candidate signal point computationsection 4210 reduces the number of candidate signal points to becomputed. In actuality, candidate signal point computation section 4210finds branch metrics for candidate signal points reduced in this way,and outputs the results as candidate signal point signal 4211 to acandidate signal point selection section 4212.

Consequently, candidate signal point computation section 4210 performsbranch metric computation only for a bit determined to have highlikelihood by partial bit determination section 4208, reducing theamount of computation. For example, when the modulation method is 16QAMin each case, MLD using conventional QR decomposition requires branchmetrics to be computed by candidate signal point computation section4210 for 16 (modulated signal b)×16 (modulated signal c)=256 points,whereas in this embodiment, when 2 bits are determined to behigh-likelihood partial bits 4209 by partial bit determination section4208, branch metrics need only be computed by candidate signal pointcomputation section 4210 for 16 (modulated signal b)×4 (modulated signalc)=64 points.

As a result, computational complexity can be reduced. In addition, sincepartial bit determination section 4208 determines a high-likelihoodpartial bit 4209 accurately as in the above-described embodiments, andreduces candidate signal points on that basis, determination areasdecrease. Consequently, both a reduction in computational complexity andan improvement in reception quality can be achieved.

Candidate signal point selection section 4212 has candidate signal pointsignal 4211 (comprising, for example, branch metrics of 64 candidatesignal points) as input, selects therefrom a previously decided numberof high-likelihood signal points (for example, 16 points) as candidatesignal points, and outputs a candidate signal point signal 4213indicating the selected candidate signal points. That is to say,candidate signal point selection section 4212 selects 16 high-likelihoodpoints based on the branch metrics of 64 candidate signal points, forexample, from candidate signal point computation section 4210, andoutputs candidate signal point signal 4213 indicating those 16 points.

Candidate signal point computation section 4214 has signal Z₁ (4202),row 1 of matrix R (4205), and selected candidate signal point signal4213 as input. Candidate signal point computation section 4214 performssignal point reduction using these, and outputs a candidate signal pointsignal 4215. This will now be explained in detail. Signal Z₁ is composedof modulated signal Tx_(a) and Tx_(b), and modulated signal Tx_(c),components. Therefore, when the signals are 16QAM signals, for example,there are 16 (modulated signal a)×16 (modulated signal b)×16 (modulatedsignal c)=4096 candidate signal points, but for modulated signal b andmodulated signal c, candidate signal point computation section 4214computes candidate signal point signal 4215 using only 4 bits selectedby candidate signal point selection section 4212. That is to say, thenumber of candidate signal points computed by candidate signal pointcomputation section 4214 is 16 (modulated signal a)×16 (modulatedsignals b, c)=256. In this way, candidate signal point computationsection 4214 reduces the number of candidate signal points to becomputed. In actuality, candidate signal point computation section 4214finds branch metrics for candidate signal points reduced in this way,and outputs the results as candidate signal point signal 4215 to acandidate signal point decision section 4216.

Candidate signal point decision section 4216 finds the most probablecandidate signal point from candidate signal point signal 4215(comprising, for example, branch metrics of 256 candidate signalpoints), and outputs a candidate signal point signal 4217 indicating themost probable signal point. This candidate signal point signal 4217corresponds, for example, to modulated signal A digital signal 322,modulated signal B digital signal 323, and modulated signal C digitalsignal 3118 in FIG. 33.

Thus, according to this embodiment, in performing MLD using QRdecomposition, by determining a high-likelihood signal by performingpartial bit determination for a QR decomposition signal (signal Z₁)containing only a single modulated signal component, and using thatdetermination result in later-stage processing, computational complexitycan be reduced without lowering bit error rate performance whenperforming MLD using QR decomposition.

In this embodiment, a case has been described in which the modulationmethod of each modulated signal is 16QAM, but this embodiment is notlimited to this case, and the same kind of effect as in theabove-described embodiment can also be obtained when the modulationmethod of each modulated signal is other than 16QAM. Also, a case hasbeen described in which there are 3 transmitting antennas and 3receiving antennas, but this embodiment is not limited to this case.

Embodiment 14

In this embodiment, a method is proposed that enables MLD using QRdecomposition to be performed with a much simpler configuration than inEmbodiment 13.

In this embodiment, a case is described by way of example in which atransmitting apparatus transmits different modulated signals A, B, and Cfrom three antennas, and a receiving apparatus receives these at threeantennas, in the same way as in Embodiment 13.

FIG. 44 shows a sample configuration of this embodiment. Signalprocessing section 4300 in FIG. 44 is used as signal processing section3117 in FIG. 32. Signal 4301 in FIG. 44 indicates a signal groupcorresponding to signals 308, 310, 3102, 306, 318, 320, 3104, 316, 3112,3114, 3116, and 3110 in FIG. 32.

QR decomposition sections 4302, 4304, and 4306 have signal group 4301 asinput, and each perform different QR decomposition.

Specifically, QR decomposition section 4302 performs the transformationin Equation (8), QR decomposition section 4304 performs thetransformation in Equation (9), and QR decomposition section 4304performs the transformation in Equation (10).

$\begin{matrix}{\begin{pmatrix}Z_{1} \\Z_{2} \\Z_{3}\end{pmatrix} = {{Q_{X}^{H}{Rx}_{X}} = {R_{X}\begin{pmatrix}{Tx}_{c} \\{Tx}_{a} \\{Tx}_{b}\end{pmatrix}}}} & (8) \\{\begin{pmatrix}Z_{1} \\Z_{2} \\Z_{3}\end{pmatrix} = {{Q_{Y}^{H}{Rx}_{Y}} = {R_{Y}\begin{pmatrix}{Tx}_{a} \\{Tx}_{b} \\{Tx}_{c}\end{pmatrix}}}} & (9) \\{\begin{pmatrix}Z_{1} \\Z_{2} \\Z_{3}\end{pmatrix} = {{Q_{Z}^{H}{Rx}_{Z}} = {R_{Z}\begin{pmatrix}{Tx}_{b} \\{Tx}_{c} \\{Tx}_{a}\end{pmatrix}}}} & (10)\end{matrix}$

Here, Rx_(X)=(Tx_(c), Tx_(a), Tx_(b))^(T), Rx_(Y)=(Tx_(a), Tx_(b),Tx_(c))^(T), Rx_(Z)=(Tx_(b), Tx_(c), Tx_(a))^(T).

Then QR decomposition section 4302 outputs Equation (8) signal Z₁,signal Z₂, signal Z₃, row 1 of matrix R, row 2 of matrix R, and row 3 ofmatrix R as signal 4303; QR decomposition section 4304 outputs Equation(9) signal Z₁, signal Z₂, signal Z₃, row 1 of matrix R, row 2 of matrixR, and row 3 of matrix R as signal 4305; and QR decomposition section4306 outputs Equation (10) signal Z₁, signal Z₂, signal Z₃, row 1 ofmatrix R, row 2 of matrix R, and row 3 of matrix R as signal 4307.

A bit unit branch metric computation section 4308 has signals 4303,4305, and 4307 from QR decomposition sections 4302, 4304, and 4306 asinput, and performs MLD for signals 4303, 4305, and 4307 by findingbranch metrics in bit units for row 2 and row 3, excluding top row 1, inmatrix R.

Specifically, bit unit branch metric computation section 4308 performsMLD for Tx_(a) and Tx_(b) by finding bit unit branch metrics based onrow 2 and row 3 of Equation (8) using signal 4303. For example, assumethat the modulation method of modulated signals A and B is QPSK. In thiscase, a branch metric is assumed to be expressed as B1 [a0] [a1] [b0][b1] [c0] [c1]. Here, a0 and a1 mean 2 transmit bits of modulated signalA, and a0, a1=0 or 1. Similarly, b0 and b1 mean 2 transmit bits ofmodulated signal B, and b0, b1=0 or 1, while c0 and c1 mean 2 transmitbits of modulated signal C, and c0, c1=0 or 1. Bit unit branch metriccomputation section 4308 finding bit unit branch metrics based on row 2and row 3 of matrix R shown in Equation (8) using signal 4303 isequivalent to finding branch metric B1 [a0] [a1] [b0] [b1] [X] [X],where X means indetermination. This is because a modulated signal Ccomponent is not included in row 2 or row 3 of Equation (8). Therefore,bit unit branch metric computation section 4308 finds a total of 16branch metrics for modulated signals A and B using signal 4303.

Similarly, bit unit branch metric computation section 4308 performs MLDfor Tx_(b) and Tx_(c) by finding bit unit branch metrics based on row 2and row 3 of Equation (9) using signal 4305. Bit unit branch metriccomputation section 4308 finding bit unit branch metrics based on row 2and row 3 of matrix R shown in Equation (9) using signal 4305 isequivalent to finding branch metric B2 [X] [X] [b0] [b1] [c0] [c1]. Inthis way, bit unit branch metric computation section 4308 finds a totalof 16 branch metrics for modulated signals B and C using signal 4305.Similarly, bit unit branch metric computation section 4308 performs MLDfor Tx_(c) and Tx_(a) by finding bit unit branch metrics based on row 2and row 3 of Equation (10) using signal 4306. Bit unit branch metriccomputation section 4308 finding bit unit branch metrics based on row 2and row 3 of matrix R shown in Equation (10) using signal 4305 isequivalent to finding branch metric B3 [a0] [a1] [X] [X] [c0] [c1]. Inthis way, bit unit branch metric computation section 4308 finds a totalof 16 branch metrics for modulated signals C and A using signal 4307.

Next, bit unit branch metric computation section 4308 adds the branchmetrics obtained as described above on a bit-by-bit basis. Assuming thata branch metric is designated Ba0,0 when bit a0 of modulated signal A is“0”, for example, branch metric Ba0,0 is found as follows.

$\begin{matrix}{{{Ba}\; 0},{0 = {{B\; {{{{{{1\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{1\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{1\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{1\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{1\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{1\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{1\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{1\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{3\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{3\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}} + {B\; {{{{{{3\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{3\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}} + {B\; {{{{{{3\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{3\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}} + {B\; {{{{{{3\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{3\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}}}}} & (11)\end{matrix}$

Assuming that a branch metric is designated Ba0,1 when bit a0 ofmodulated signal A is “1”, bit unit branch metric computation section4308 finds branch metric Ba0,1 as follows.

$\begin{matrix}{{{Ba}\; 0},{1 = {{B\; {{{{{{1\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{1\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{1\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{1\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{1\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{1\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{1\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{1\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{3\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{3\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}} + {B\; {{{{{{3\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{3\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}} + {B\; {{{{{{3\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{3\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}} + {B\; {{{{{{3\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{3\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}}}}} & (12)\end{matrix}$

Bit unit branch metric computation section 4308 finds branch metricsBa1,0 and Ba1,1 when bit a1 of modulated signal A is “0” and “1” in asimilar way.

Assuming that a branch metric is designated Bb0,0 when bit b0 ofmodulated signal B is “0”, bit unit branch metric computation section4308 finds branch metric Bb0,0 as follows.

$\begin{matrix}{{{Bb}\; 0},{0 = {{B\; {{{{{{1\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{1\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{1\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{1\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{1\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{1\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{1\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{1\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{2\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{2\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}} + {B\; {{{{{{2\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{2\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}} + {B\; {{{{{{2\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{2\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}} + {B\; {{{{{{2\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{2\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}}}}} & (13)\end{matrix}$

Assuming that a branch metric is designated Bb0,1 when bit b0 ofmodulated signal B is “1”, bit unit branch metric computation section4308 finds branch metric Bb0,1 as follows.

$\begin{matrix}{{{Bb}\; 0},{1 = {{B\; {{{{{{1\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{1\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{1\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{1\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{1\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{1\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{1\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{1\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{2\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{2\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}} + {B\; {{{{{{2\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{2\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}} + {B\; {{{{{{2\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{2\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}} + {B\; {{{{{{2\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{2\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}}}}} & (14)\end{matrix}$

Bit unit branch metric computation section 4308 finds branch metricsBb1,0 and Bb1,1 when bit b1 of modulated signal B is “0” and “1” in asimilar way.

Assuming that a branch metric is designated Bc0,0 when bit c0 ofmodulated signal C is “0”, bit unit branch metric computation section4308 finds branch metric Bc0,0 as follows.

$\begin{matrix}{{{Bc}\; 0},{0 = {{B\; {{{{{{2\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{2\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{2\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{2\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{2\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}} + {B\; {{{{{{2\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}} + {B\; {{{{{{2\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}} + {B\; {{{{{{2\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}} + {B\; {{{{{{3\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{3\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{3\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{3\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{3\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}} + {B\; {{{{{{3\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}} + {B\; {{{{{{3\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}} + {B\; {{{{{{3\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}}}}} & (15)\end{matrix}$

Assuming that a branch metric is designated Bc0,1 when bit c0 ofmodulated signal C is “1”, bit unit branch metric computation section4308 finds branch metric Bc0,1 as follows.

$\begin{matrix}{{{Bc}\; 0},{1 = {{B\; {{{{{{2\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{2\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{2\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{2\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{2\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}} + {B\; {{{{{{2\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}} + {B\; {{{{{{2\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}} + {B\; {{{{{{2\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}} + {B\; {{{{{{3\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{3\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{3\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{3\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{3\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}} + {B\; {{{{{{3\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}} + {B\; {{{{{{3\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}} + {B\; {{{{{{3\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}}}}} & (15)\end{matrix}$

Bit unit branch metric computation section 4308 finds branch metricsBc1,0 and Bc1,1 when bit c1 of modulated signal C is “0” and “1” in asimilar way.

Then bit unit branch metric computation section 4308 outputs the valuesobtained as described above as a modulated signal A bit unit branchmetric group signal 4309, a modulated signal B bit unit branch metricgroup signal 4310, and a modulated signal C bit unit branch metric groupsignal 4311.

A determination section 4312 determines the most probable signal pointfor modulated signals A, B, and C based on modulated signal A bit unitbranch metric group signal 4309, modulated signal B bit unit branchmetric group signal 4310, and modulated signal C bit unit branch metricgroup signal 4311, and outputs the determination result as receive data4313. This receive data 4313 corresponds, for example, to modulatedsignal A digital signal 322, modulated signal B digital signal 323, andmodulated signal C digital signal 3118 in FIG. 33.

Thus, according to this embodiment, by providing a plurality of QRdecomposition sections 4302, 4304, and 4306 that perform different QRdecomposition, a bit unit branch metric computation section 4308 thatfinds branch metrics for signals 4303, 4305, and 4307 obtained by QRdecomposition sections 4302, 4304, and 4306 based on rows other than thetop row (in the example in this embodiment, row 2 and row 3 of matrix R,excluding row 1), and a determination section 4312 that performslikelihood determination based on those branch metrics, computationalcomplexity can be reduced without lowering bit error rate performancewhen performing MLD using QR decomposition.

As compared with a configuration that narrows down candidate signalpoints in stages, such as shown in FIG. 43, for example, the fact that aconfiguration in which candidate signal points are passed on is notnecessary enables computational complexity to be simplified and alsoreduces delay due to computation, making high-speed operation possible.

Differences in the design concept of MLD that omits the top row in QRdecomposition according to this embodiment, and conventional QR-MLD,will now be mentioned. In a system with 3 transmitting antennas and 3receiving antennas, for example, conventional QR-MLD provides an MLDapproximation algorithm for obtaining diversity gain of the 3 receivingantennas. On the other hand, this embodiment can be said to provide anMLD approximation algorithm for obtaining diversity of 2 receivingantennas. That is to say, generally speaking, in a system with Mtransmitting antennas and M receiving antennas, conventional QR-MLDprovides an MLD approximation algorithm for obtaining diversity gain ofM receiving antennas, whereas this embodiment provides an MLDapproximation algorithm for obtaining diversity of M-P receivingantennas (where M>P).

A characteristic of this embodiment is that, as described above,computational complexity can be reduced compared with conventionalQR-MLD. A further characteristic is that, when above value M is large, amajor difference in reception quality compared with that of conventionalQR-MLD does not occur as long as P is set to a low value.

Incidentally, MMSE extension can also be implemented in a similar way asan MLD method using QR decomposition. This is a known technologymentioned, for example, in “Investigation of QRM-MLD Performing MMSEExtension in a Single-User/Multi-User MIMO Transmission Method” IEICETechnical Report RCS2005-190, March 2006, pp. 73-78, and therefore adescription thereof is omitted here.

With this embodiment, computational complexity can be furthersignificantly reduced by adding a configuration that executes partialbit determination on the bottom row in the same way as in Embodiment 13.

In this embodiment, a case has been described by way of example in whichthe number of modulated signals is 3, but this embodiment is not limitedto this case, and can also be implemented in the same way when there are4 or more modulated signals. For example, this embodiment can also beapplied to a case in which a transmitting apparatus transmits differentmodulated signals from 4 antennas. In this case, whereas MLD wasperformed on row 2 and row 3 after QR decomposition in theabove-described embodiment, MLD may be executed on row 3 and row 4 afterQR decomposition, or MLD may be executed on row 2, row 3, and row 4after QR decomposition. That is to say, MLD should be performed based onrows other than the top row(s) after QR decomposition.

In this embodiment, a case has been described in which the modulationmethod of each modulated signal is QPSK, but this embodiment is notlimited to this case.

The method of finding branch metrics is not limited to an adding methodsuch as described above. For example, branch metric Ba0,0 for a case inwhich bit a0 of modulated signal A is “0” may be taken as the minimumvalue among the following: B1[0] [0] [0] [0] [X] [X], B1 [0] [0] [0] [1][X] [X], B1 [0] [0] [1] [0] [X] [X], B1 [0] [0] [1] [1] [X] [X], B1 [0][1] [0] [0] [X] [X], B1 [0] [1] [0] [1] [X] [X], B1 [0] [1] [1] [0] [X][X], B1 [0] [1] [1] [1] [X] [X], B3 [0] [0] [X] [X] [0] [0], B3 [0] [0][X] [X] [0] [1], B3 [0] [0] [X] [X] [1] [0], B3 [0] [0] [X] [X] [1] [1],B3 [0] [1] [X] [X] [0] [0], B3 [0] [1] [X] [X] [0] [1], B3 [0] [1] [X][X] [1] [0], B3 [0] [1] [X] [X] [1] [1].

A branch metric found in this embodiment corresponds to a posteriorprobability when decoded. The use of prior probabilities will enablereception quality to be significantly improved. In order to use priorprobabilities in this embodiment, it is necessary to find the inversematrix of the matrix formed by row 2 and row 3 in Equation (8), Equation(9), and Equation (10), for example, find the SNR after separation fromthat inverse matrix (see above Equation (3) and Equation (4)), and usethis.

Embodiment 15

In this embodiment, a configuration and method are proposed that enablecomputational complexity to be reduced without lowering bit error rateperformance when performing MLD using QR decomposition by providing aplurality of QR decomposition sections that perform different QRdecomposition in basically the same way as in Embodiment 14. Thisembodiment differs from Embodiment 14 with regard to the nature of theplurality of QR decompositions.

In this embodiment, a case is described by way of example in which atransmitting apparatus transmits different modulated signals A, B, and Cfrom three antennas, and a receiving apparatus receives these at threeantennas, in the same way as in Embodiment 14.

FIG. 45 shows a sample configuration of this embodiment. Signalprocessing section 4400 in FIG. 45 is used as signal processing section3117 in FIG. 32. Signal 4301 in FIG. 45 indicates a signal groupcorresponding to signals 308, 310, 3102, 306, 318; 320, 3104, 316, 3112,3114, 3116, and 3110 in FIG. 32.

QR decomposition sections 4302, 4304, and 4306 have signal group 4301 asinput, and each perform different QR decomposition.

Specifically, QR decomposition section 4401A performs the transformationin Equation (17), QR decomposition section 4402A performs thetransformation in Equation (18), QR decomposition section 4401B performsthe transformation in Equation (19), QR decomposition section 4402Bperforms the transformation in Equation (20), QR decomposition section4401C performs the transformation in Equation (21), and QR decompositionsection 4402C performs the transformation in Equation (22).

$\begin{matrix}{\begin{pmatrix}Z_{1} \\Z_{2} \\Z_{3}\end{pmatrix} = {{Q_{a\; 1}^{H}{Rx}_{a\; 1}} = {R_{a\; 1}\begin{pmatrix}{Tx}_{c} \\{Tx}_{b} \\{Tx}_{a}\end{pmatrix}}}} & (17) \\{\begin{pmatrix}Z_{1} \\Z_{2} \\Z_{3}\end{pmatrix} = {{Q_{a\; 2}^{H}{Rx}_{a\; 2}} = {R_{a\; 2}\begin{pmatrix}{Tx}_{b} \\{Tx}_{c} \\{Tx}_{a}\end{pmatrix}}}} & (18) \\{\begin{pmatrix}Z_{1} \\Z_{2} \\Z_{3}\end{pmatrix} = {{Q_{b\; 1}^{H}{Rx}_{b\; 1}} = {R_{b\; 1}\begin{pmatrix}{Tx}_{c} \\{Tx}_{a} \\{Tx}_{b}\end{pmatrix}}}} & (19) \\{\begin{pmatrix}Z_{1} \\Z_{2} \\Z_{3}\end{pmatrix} = {{Q_{b\; 2}^{H}{Rx}_{b\; 2}} = {R_{b\; 2}\begin{pmatrix}{Tx}_{a} \\{Tx}_{c} \\{Tx}_{b}\end{pmatrix}}}} & (20) \\{\begin{pmatrix}Z_{1} \\Z_{2} \\Z_{3}\end{pmatrix} = {{Q_{c\; 1}^{H}{Rx}_{c\; 1}} = {R_{c\; 1}\begin{pmatrix}{Tx}_{b} \\{Tx}_{a} \\{Tx}_{c}\end{pmatrix}}}} & (21) \\{\begin{pmatrix}Z_{1} \\Z_{2} \\Z_{3}\end{pmatrix} = {{Q_{c\; 2}^{H}{Rx}_{c\; 2}} = {R_{c\; 2}\begin{pmatrix}{Tx}_{a} \\{Tx}_{b} \\{Tx}_{c}\end{pmatrix}}}} & (22)\end{matrix}$

Here, Rx_(a1)=(Tx_(c), Tx_(b), Tx_(a))^(T), Rx_(a2)=(Tx_(b), Tx_(c),Tx_(a))^(T), Rx_(b1)=(Tx_(c), Tx_(a), Tx_(b))^(T), Rx_(b2)=(Tx_(a),Tx_(c), Tx_(b))^(T), Rx_(c1)=(Tx_(a), Tx_(b), Tx_(c))^(T),Rx_(c2)=(Tx_(a), Tx_(b), Tx_(c))^(T).

Then QR decomposition section 4401A outputs Equation (17) signal Z₁,signal Z₂, signal Z₃, row 1 of matrix R, row 2 of matrix R, and row 3 ofmatrix R as signal 4403A; QR decomposition section 4402A outputsEquation (18) signal Z₁, signal Z₂, signal Z₃, row 1 of matrix R, row 2of matrix R, and row 3 of matrix R as signal 4404A; QR decompositionsection 4401B outputs Equation (19) signal Z₁, signal Z₂, signal Z₃, row1 of matrix R, row 2 of matrix R, and row 3 of matrix R as signal 4403B;QR decomposition section 4402B outputs Equation (20) signal Z₁, signalZ₂, signal Z₃, row 1 of matrix R, row 2 of matrix R, and row 3 of matrixR as signal 4404B; QR decomposition section 4401C outputs Equation (21)signal Z₁, signal Z₂, signal Z₃, row 1 of matrix R, row 2 of matrix R,and row 3 of matrix R as signal 4403C; and QR decomposition section4402C outputs Equation (22) signal Z₁, signal Z₂, signal Z₃, row 1 ofmatrix R, row 2 of matrix R, and row 3 of matrix R as signal 4404C.

A bit unit branch metric computation section 4405A has signals 4403A and4404A from QR decomposition sections 4401A and 4402A as input, andperforms MLD for signals 4403A and 4404A by finding branch metrics inbit units for row 2 and row 3, excluding top row 1, in matrix R.

Specifically, bit unit branch metric computation section 4405A performsMLD for Tx_(a) and Tx_(b) by finding bit unit branch metrics based onrow 2 and row 3 of Equation (17) using signal 4403A. Also, bit unitbranch metric computation section 4405A performs MLD for Tx_(a) andTx_(c) by finding bit unit branch metrics based on row 2 and row 3 ofEquation (18) using signal 4404A. For example, when the modulationmethod of each modulated signal is QPSK, bit unit branch metriccomputation section 4405A finds bit unit branch metric B1 [a0] [a1] [b0][b1] [X] [X] based on row 2 and row 3 of matrix R shown in Equation (17)using signal 4403A. That is to say, bit unit branch metric computationsection 4405A finds a total of 16 branch metrics for modulated signals Aand B using signal 4403A. Also, bit unit branch metric computationsection 4405A finds bit unit branch metric B2 [a0] [a1] [X] [X] [c0][c1] based on row 2 and row 3 of matrix R shown in Equation (18) usingsignal 4404A. That is to say, bit unit branch metric computation section4405A finds a total of 16 branch metrics for modulated signals A and Cusing signal 4404A.

Next, bit unit branch metric computation section 4405A adds the branchmetrics obtained as described above on a bit-by-bit basis. Assuming thata branch metric is designated Ba0,0 when bit a0 of modulated signal A is“0”, for example, branch metric Ba0,0 is found as follows.

$\begin{matrix}{{{Ba}\; 0},{0 = {{B\; {{{{{{1\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{1\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{1\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{1\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{1\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{1\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{1\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{1\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{2\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{2\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}} + {B\; {{{{{{2\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{2\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}} + {B\; {{{{{{2\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{2\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}} + {B\; {{{{{{2\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{2\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}}}}} & (23)\end{matrix}$

Assuming that a branch metric is designated Ba0,1 when bit a0 ofmodulated signal A is “1”, bit unit branch metric computation section4405A finds branch metric Ba0,1 as follows.

$\begin{matrix}{{{Ba}\; 0},{1 = {{B\; {{{{{{1\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{1\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{1\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{1\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{1\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{1\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{1\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{1\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{2\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{2\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}} + {B\; {{{{{{2\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{2\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}} + {B\; {{{{{{2\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{2\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}} + {B\; {{{{{{2\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{2\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}}}}} & (24)\end{matrix}$

Bit unit branch metric computation section 4405A finds branch metricsBa1,0 and Ba1,1 when bit a1 of modulated signal A is “0” and “1” in asimilar way, and then outputs the values obtained as described above asa modulated signal A bit unit branch metric group signal 4406A.

A bit unit branch metric computation section 4405B has signals 4403B and4404B from QR decomposition sections 4401B and 4402B as input, andperforms MLD for signals 4403B and 4404B by finding branch metrics inbit units for row 2 and row 3, excluding top row 1, in matrix R.

Specifically, bit unit branch metric computation section 4405B performsMLD for Tx_(b) and Tx_(a) by finding bit unit branch metrics based onrow 2 and row 3 of Equation (19) using signal 4403B. Also, bit unitbranch metric computation section 4405B performs MLD for Tx_(b) andTx_(c) by finding bit unit branch metrics based on row 2 and row 3 ofEquation (20) using signal 4404B. For example, when the modulationmethod of each modulated signal is QPSK, bit unit branch metriccomputation section 4405B finds bit unit branch metric B3 [a0] [a1] [b0][b1] [X] [X] based on row 2 and row 3 of matrix R shown in Equation (19)using signal 4403B. That is to say, bit unit branch metric computationsection 4405B finds a total of 16 branch metrics for modulated signals Band A using signal 4403B. Also, bit unit branch metric computationsection 4405B finds bit unit branch metric B4 [X] [X] [b0] [b1] [c0][c1] based on row 2 and row 3 of matrix R shown in Equation (20) usingsignal 4404B. That is to say, bit unit branch metric computation section4405B finds a total of 16 branch metrics for modulated signals B and Cusing signal 4404B.

Next, bit unit branch metric computation section 4405B adds the branchmetrics obtained as described above on a bit-by-bit basis. Assuming thata branch metric is designated Bb0,0 when bit b0 of modulated signal B is“0”, for example, branch metric Bb0,0 is found as follows.

$\begin{matrix}{{{Bb}\; 0},{0 = {{B\; {{{{{{3\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{3\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{3\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{3\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{3\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{3\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{3\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{3\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{4\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{4\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}} + {B\; {{{{{{4\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{4\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}} + {B\; {{{{{{4\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{4\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}} + {B\; {{{{{{4\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{4\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}}}}} & (25)\end{matrix}$

Assuming that a branch metric is designated Bb0,1 when bit b0 ofmodulated signal B is “1”, bit unit branch metric computation section4405B finds branch metric Bb0,1 as follows.

$\begin{matrix}{{{Bb}\; 0},{1 = {{B\; {{{{{{3\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{3\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{3\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{3\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{3\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{3\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{3\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{3\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}} + {B\; {{{{{{4\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{4\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}} + {B\; {{{{{{4\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{4\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}} + {B\; {{{{{{4\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{4\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}} + {B\; {{{{{{4\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{4\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}}}}} & (26)\end{matrix}$

Bit unit branch metric computation section 4405B finds branch metricsBb1,0 and Bb1,1 when bit b1 of modulated signal B is “0” and “1” in asimilar way, and then outputs the values obtained as described above asa modulated signal B bit unit branch metric group signal 4406B.

A bit unit branch metric computation section 4405C has signals 4403C and4404C from QR decomposition sections 4401C and 4402C as input, andperforms MLD for signals 4403C and 4404C by finding branch metrics inbit units for row 2 and row 3, excluding top row 1, in matrix R.

Specifically, bit unit branch metric computation section 4405C performsMLD for Tx_(c) and Tx_(a) by finding bit unit branch metrics based onrow 2 and row 3 of Equation (21) using signal 4403C. Also, bit unitbranch metric computation section 4405C performs MLD for Tx_(c) andTx_(b) by finding bit unit branch metrics based on row 2 and row 3 ofEquation (22) using signal 4404C. For example, when the modulationmethod of each modulated signal is QPSK, bit unit branch metriccomputation section 4405C finds bit unit branch metric B3 [a0] [a1] [X][X] [c0] [c1] based on row 2 and row 3 of matrix R shown in Equation(21) using signal 4403C. That is to say, bit unit branch metriccomputation section 4405C finds a total of 16 branch metrics formodulated signals C and A using signal 4403C. Also, bit unit branchmetric computation section 4405C finds bit unit branch metric B6 [X] [X][b0] [b1] [c0] [c1] based on row 2 and row 3 of matrix R shown inEquation (22) using signal 4404C. That is to say, bit unit branch metriccomputation section 4405C finds a total of 16 branch metrics formodulated signals C and B using signal 4404C.

Next, bit unit branch metric computation section 4405C adds the branchmetrics obtained as described above on a bit-by-bit basis. Assuming thata branch metric is designated Bc0,0 when bit c0 of modulated signal C is“0”, for example, branch metric Bc0,0 is found as follows.

$\begin{matrix}{{{Bc}\; 0},{0 = {{B\; {{{{{{6\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{6\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{6\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{6\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{6\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}} + {B\; {{{{{{6\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}} + {B\; {{{{{{6\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}} + {B\; {{{{{{6\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}} + {B\; {{{{{{5\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{5\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{5\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{5\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{5\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}} + {B\; {{{{{{5\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}} + {B\; {{{{{{5\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}} + {B\; {{{{{{5\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}}}}} & (27)\end{matrix}$

Assuming that a branch metric is designated Bc0,1 when bit c0 ofmodulated signal C is “1”, bit unit branch metric computation section4405C finds branch metric Bc0,1 as follows.

$\begin{matrix}{{{Bc}\; 0},{1 = {{B\; {{{{{{6\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{6\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{6\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{6\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{6\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}} + {B\; {{{{{{6\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}} + {B\; {{{{{{6\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}} + {B\; {{{{{{6\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}} + {B\; {{{{{{5\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{5\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{5\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{5\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 0\rbrack}} + {B\; {{{{{{5\lbrack 0\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}} + {B\; {{{{{{5\lbrack 0\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}} + {B\; {{{{{{5\lbrack 1\rbrack}\lbrack 0\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}} + {B\; {{{{{{5\lbrack 1\rbrack}\lbrack 1\rbrack}\lbrack X\rbrack}\lbrack X\rbrack}\lbrack 1\rbrack}\lbrack 1\rbrack}}}}} & (28)\end{matrix}$

Bit unit branch metric computation section 4405C finds branch metricsBc1,0 and Bc1,1 when bit c1 of modulated signal C is “0” and “1” in asimilar way, and then outputs the values obtained as described above asa modulated signal C bit unit branch metric group signal 4406C.

A determination section 4407 determines the most probable signal pointfor modulated signals A, B, and C based on modulated signal A bit unitbranch metric group signal 4406A, modulated signal B bit unit branchmetric group signal 4406B, and modulated signal C bit unit branch metricgroup signal 4406C, and outputs the determination result as receive data4408. This receive data 4408 corresponds, for example, to modulatedsignal A digital signal 322, modulated signal B digital signal 323, andmodulated signal C digital signal 3118 in FIG. 33.

Comparing the configuration of Embodiment 14 with the configuration ofthis embodiment, in Embodiment 14 QR decomposition is performed byswitching all rows among QR decomposition sections 4302, 4304, and 4306,whereas in this embodiment QR decomposition is performed by keeping thebottom row (row 3) fixed and switching the other rows between QRdecomposition sections 4401A and 4402A, between QR decompositionsections 4401B and 4402B, and between QR decomposition sections 4401Cand 4402C.

Thus, according to this embodiment, by providing a plurality of QRdecomposition sections 4401A, 4402A, 4401B, 4402B, 4401C, and 4402C thatperform different QR decomposition, bit unit branch metric computationsections 4405A, 4405B, and 4405C that find branch metrics for signals4403A, 4404A, 4403B, 4404B, 4403C, and 4404C obtained by QRdecomposition sections 4401A, 4402A, 4401B, 4402B, 4401C, and 4402Cbased on rows other than the top row (in the example in this embodiment,row 2 and row 3 of matrix R, excluding row 1), and a determinationsection 4407 that performs likelihood determination based on thosebranch metrics, computational complexity can be reduced without loweringbit error rate performance when performing MLD using QR decomposition.

As compared with a configuration that narrows down candidate signalpoints in stages, such as shown in FIG. 43, for example, the fact that aconfiguration in which candidate signal points are passed on is notnecessary enables computational complexity to be simplified and alsoreduces delay due to computation, making high-speed operation possible.

In this embodiment, a case has been described by way of example in whichthe number of modulated signals is 3, but this embodiment is not limitedto this case, and can also be implemented in the same way when there are4 or more modulated signals. With 3 modulated signals, MLD is performedfor row 2 and row 3 after QR decomposition, and QR decomposition isperformed so that a modulated signal to be found belongs to the bottomrow, row 3, but when, for example, a transmitting apparatus transmitsdifferent modulated signals from four antennas, MLD can be executed forrow 3 and row 4 after QR decomposition, and QR decomposition performedso that a modulated signal to be found belongs to the bottom row, row 4.In this case, for example, MLD can be performed for row 2, row 3, androw 4 after QR decomposition, and QR decomposition performed so that amodulated signal to be found belongs to the bottom row, row 4.

In this embodiment, a case has been described in which the modulationmethod of each modulated signal is QPSK, but this embodiment is notlimited to this case.

The method of finding branch metrics is not limited to an adding methodsuch as described above. For example, branch metric Ba0,0 for a case inwhich bit a0 of modulated signal A is “0” may be taken as the minimumvalue among the following: B1 [0] [0] [0] [0] [X] [X], B1 [0] [0] [0][1] [X] [X], B1 [0] [0] [1] [0] [X] [X], B1 [0] [0] [1] [1] [X] [X], B1[0] [1] [0] [0] [X] [X], B1 [0] [1] [0] [1] [X] [X], B1 [0] [1] [1] [0][X] [X], B1 [0] [1] [1] [1] [X] [X], B2 [0] [0] [X] [X] [0] [0], B2 [0][0] [X] [X] [0] [1], B2 [0] [0] [X] [X] [1] [0], B2 [0] [0] [X] [X] [1][1], B2 [0] [1] [X] [X] [0] [0], B2 [0] [1] [X] [X] [0] [1], B2 [0] [1][X] [X] [1] [0], B2 [0] [1] [X] [X] [1] [1].

A branch metric found in this embodiment corresponds to a posteriorprobability when decoded. The use of prior probabilities will enablereception quality to be significantly improved. In order to use priorprobabilities in this embodiment, it is necessary to find the inversematrix of the matrix formed by row 2 and row 3 in Equation (18),Equation (19), Equation (20), Equation (21), and Equation (22), forexample, find the SNR after separation from that inverse matrix (seeabove Equation (3) and Equation (4)), and use this.

In this embodiment a case has been described in which QR decompositionis performed for all combinations in which each modulated signal isfixed in the bottom row and the other rows are switched around, andbranch metrics are found, but it is not absolutely for QR decompositionand branch metric computation to be performed for all combinations inwhich each modulated signal is the bottom row. For example, in thisembodiment, to consider modulated signal A, QR decomposition isperformed for all cases in which modulated signal A is the bottomrow—that is, for Equation (17) and Equation (18). However, a modulatedsignal A branch metric may be found with Equation (17) only, enablingthe computational complexity to be reduced. In particular, the number oftimes QR decomposition is performed when the number of transmitmodulated signals increases leads to a sharp increase in computationalcomplexity, and therefore setting the number of QR decompositionprocessing operations as appropriate is effective from the standpoint ofreducing computational complexity. The computational complexity can beeffectively reduced if, for example, the number of QR decompositionprocessing operations is increased for a more important modulatedsignal, and decreased for a less important modulated signal.

Other Embodiments

In the above embodiments, cases have mainly been described, by way ofexample, in which the present invention is applied to spread spectrumcommunication scheme and OFDM scheme. But the present invention is notlimited to these cases, and similar effects can also be obtained when asingle-carrier system or a multicarrier system other than OFDM, or asystem combining use of a multicarrier system and spread spectrumcommunication scheme with MIMO transmission applied therein, is used.

Also, although cases in which modulation which has 16 signal points isused as the modulation method have mainly been described, similareffects can also be obtained when M-ary modulation other than modulationwhich has 16 signal points is used. That is to say, in the aboveembodiments, partial bits have been found as shown in FIG. 9B, FIG. 11B,and FIG. 15B when a modulation signal which has 16 signal points isreceived, but this is not a limitation. The same kind of effects as inthe above-described embodiments can be obtained when, in the case of anm-value modulation method that transmits m bits in 1 symbol, forexample, m bits are reduced to m-k bits based on k (k<m) bits found bymeans of partial bit determination (that is, the number of candidatesignal points is reduced), and likelihood detection is performed for thereduced candidate signal points. Furthermore, the area division methodused when finding partial bits is not limited to the method in FIG. 9B,FIG. 11B, FIG. 15B, FIG. 17, FIG. 18, or FIG. 19, and a differentdivision method can be applied.

In the above embodiments, cases have mainly been described in whichinverse matrix computations are performed in determining partial bits,but the partial bit determination method is not limited to this, and,essentially, the same kind of effects as in the above-describedembodiments can be obtained as long as partial bits are found by meansof a detection method different from likelihood detection and adetection method involving a smaller amount of computation thanlikelihood decoding, since the amount of computation can be reducedcompared with a case in which all bits are found by means of likelihooddetection.

Furthermore, in the above embodiments, a case has generally beendescribed, by way of example, in which the number of transmittingantennas is 2, the number of receiving antennas is 2, and the number oftransmit modulated signals is 2, but the present invention is notlimited to this case, and can also be applied to an apparatus with ntransmitting antennas, n receiving antennas, and n transmit signals(where n≧3). Moreover, the present invention can also be applied to anapparatus aimed at improving the degree of separation and/or receptionquality by using more receiving antennas than transmitting antennas andtransmit signals, and performing combining or selection diversity whenperforming separation and signal point reduction.

The present invention is not limited to the above-described embodiments,and various variations and modifications may be possible withoutdeparting from the scope of the present invention.

According to one aspect of a receiving apparatus of the presentinvention, a receiving apparatus that receives modulated signalstransmitted from a transmitting apparatus that transmits differentmodulated signals from a plurality of antennas employs a configurationthat includes: a channel fluctuation estimation section that finds achannel estimate of each modulated signal; a partial bit demodulationsection that demodulates only some bits of a modulated signal using adetection method different from likelihood detection; a signal pointreduction section that reduces the number of candidate signal pointsusing demodulated partial bits and a channel estimate; and a likelihooddetection section that performs likelihood detection using a reducednumber of candidate signal points and a received baseband signal.

According to this configuration, since demodulation of only some bits isperformed by the partial bit demodulation section using a detectionmethod different from likelihood detection, partial bits can be obtainedwith a small amount of computation. Also, since likelihood detection isperformed by the likelihood detection section using a reduced number ofcandidate signal points, the remaining bits can be found with a highdegree of precision using a small amount of computation. As likelihooddetection is performed on a partial basis in this way, received digitalsignals with good bit error rate performances can be obtained whilereducing the number of computations for finding Euclidian distances.

According to one aspect of a receiving apparatus of the presentinvention, a configuration is employed that further includes a controlsection that controls which modulated signals' partial bits are used forcandidate signal point reduction by a signal point reduction sectionbased on the reception quality of each modulated signal.

According to this configuration, compared with a case in which signalpoint reduction is performed by simply using partial bits of allmodulated signals, it is possible to provide for partial bits with ahigh probability of being erroneous not to be used in signal pointreduction processing, enabling more accurate signal point reductionprocessing to be performed, and received digital signals withsignificantly better bit error rate performances to be obtained.

According to one aspect of a receiving apparatus of the presentinvention, a configuration is employed that further includes a controlsection that controls how many partial bits of each modulated signal areused for candidate signal point reduction by a signal point reductionsection based on the reception quality of each modulated signal.

According to this configuration, it is possible to provide for partialbits with a high probability of being erroneous not to be used in signalpoint reduction processing, enabling more accurate signal pointreduction processing to be performed, and received digital signals withsignificantly better bit error rate performances to be obtained,compared with a case in which signal point reduction is performed bysimply using the same number of partial bits for all modulated signals.

According to one aspect of a receiving apparatus of the presentinvention, a partial bit demodulation section employs a configurationthat includes: a separation section that separates a received signalinto modulated signals; and a partial bit determination section thatfinds a candidate signal point for which the Euclidian distance from theseparated modulated signal reception point is a minimum, inverts thebits contained in the bit string corresponding to the found candidatesignal point one at a time, searches, for each inverted bit, for aplurality of candidate signal points containing the inverted bit,detects, for each inverted bit, the minimum Euclidian distance betweenthe reception point and the aforementioned plurality of candidate signalpoints, detects the maximum Euclidian distance among the minimumEuclidian distances of each aforementioned inverted bit, and determines1 bit corresponding to the detected maximum Euclidian distance to be ademodulation partial bit.

According to this configuration, 1 bit with an extremely low probabilityof being erroneous can be obtained by the partial bit determinationsection, enabling more accurate signal point reduction processing to beperformed, and received digital signals with significantly better biterror rate performances to be obtained.

According to one aspect of a receiving apparatus of the presentinvention, a partial bit demodulation section employs a configurationthat includes: a separation section that separates modulated signals byperforming inverse matrix computation on a channel estimation matrixusing a channel estimate; and a partial bit determination section thatdetermines partial bits of a separated modulated signal.

According to one aspect of a receiving apparatus of the presentinvention, a partial bit determination section employs a configurationthat includes: a separation section that separates modulated signals byperforming MMSE (Minimum Mean Square Error) computation on a channelestimation matrix using a channel estimate; and a partial bitdetermination section that determines partial bits of separatedmodulated signals.

According to these configurations, partial bits can be determined usinga small amount of computation compared with a case of likelihooddetection.

One aspect of a partial bit determination method of the presentinvention includes: a minimum distance candidate point detecting step ofdetecting a candidate signal point for which the Euclidian distance froma modulated signal reception point is a minimum; an inverting step ofinverting the bits contained in the bit string corresponding to thedetected candidate signal point one at a time; a step of searching, foreach inverted bit, for a plurality of candidate signal points containingthe inverted bit; a step of detecting, for each inverted bit, theminimum Euclidian distance between the reception point and theaforementioned found plurality of candidate signal points; a step ofdetecting the maximum Euclidian distance among the minimum Euclidiandistances of each inverted bit; and a step of determining the bitcorresponding to the detected maximum Euclidian distance to be a partialbit.

According to this method, the bit with the highest reliability can bedetermined within a bit string represented by a candidate signal pointdetected in the minimum distance candidate point detecting step,enabling 1 bit with an extremely low probability of being erroneous tobe determined.

According to one aspect of a transmitting apparatus of the presentinvention, a transmitting apparatus that transmits different modulatedsignals from a plurality of antennas employs a configuration thatincludes: a modulation section that obtains a modulated signal byperforming signal point mapping of transmit bits using a signal pointarrangement that is divided into a plurality of signal point sets on theIQ plane, and whereby the minimum distance between signal points withina signal point set is smaller than the minimum signal point distancebetween signal point sets; and an antenna that transmits a modulatedsignal obtained by the modulation section.

According to this configuration, a bit common to signal points within asignal set can be determined easily and accurately on the receivingside. Thus, an extremely convenient transmit signal can be formed for areceiving apparatus for which demodulation of only some bits (partialbits) of a modulated signal is required.

According to one aspect of a transmitting apparatus of the presentinvention, a configuration is employed that further includes a codingsection that codes transmit bits mapped within the same signal point settogether.

According to this configuration, error correction processing can beperformed on the receiving side in partial bit units common within asignal point set, enabling partial bits with a significantly lowerprobability of being erroneous to be obtained on the receiving side witha much simpler configuration.

According to one aspect of a transmitting apparatus of the presentinvention, the coding section employs a configuration that executescoding with higher error correction capability for transmit bits mappedwithin the same signal point set than for other transmit bits.

According to this configuration, partial bits with a significantly lowerprobability of being erroneous can be obtained on the receiving side.

According to one aspect of a receiving apparatus of the presentinvention, a partial bit demodulation section employs a configurationwherein, in demodulating some bits of a modulated signal that hasundergone 64QAM modulation, which bit in a 6-bit bit string making upone symbol is demodulated as a partial bit is changed according to whicharea on the IQ plane the relevant reception signal point is present in.

According to this configuration, the bit error performance of a partialbit demodulated by the partial bit demodulation section improves, andtherefore the reliability of reduced candidate signal points used by thelikelihood detection section improves. As a result, the bit errorperformance of final demodulated bits can be improved.

According to one aspect of a receiving apparatus of the presentinvention, a configuration is employed that includes: a plurality of QRdecomposition sections that perform different QR decomposition; a bitunit branch metric computation section that finds branch metrics forsignals obtained by the QR decomposition sections based on rows otherthan the top row; and a determination section that performs likelihooddetermination based on those branch metrics.

According to this configuration, computational complexity can be reducedwithout lowering bit error rate performance when performing MLD using QRdecomposition.

The present application is based on Japanese Patent Application No.2005-153164 filed on May 25, 2005, and Japanese Patent Application No.2006-70044 filed on Mar. 14, 2006, the entire content of which isexpressly incorporated herein by reference.

INDUSTRIAL APPLICABILITY

A receiving apparatus of the present invention can be widely applied toradio communication systems in which different modulated signals aretransmitted from a plurality of antennas, such as a MIMO (Multiple-InputMultiple-Output) system or OFDM-MIMO system, for example.

1-4. (canceled)
 5. A receiving apparatus that receives a modulatedsignal transmitted from a transmitting apparatus, said receivingapparatus comprising: a channel fluctuation estimation section thatfinds a channel estimate of each modulated signal; and a Euclidiandistance calculation section that has said channel estimate and saidreceived signal as input, finds a candidate signal point from saidchannel estimate, and calculates an approximation of a Euclidiandistance between said candidate signal point and said received signalusing a bit shift and an adder.
 6. The receiving apparatus according toclaim 5, wherein said Euclidian distance calculation section: outputs aEuclidian distance approximation as |y| when a size relationship of anI-direction distance x and Q-direction distance y for said candidatesignal point and said reception signal point on an IQ plane is |x|<|y|and is also 0<|x|j|y|×(1/4+1/8), outputs a Euclidian distanceapproximation as |y|×(1+1/8) when said size relationship is |x|<|y| andis also |y|×(1/4+1/8)<|x|<|y|×(1/2+1/8), outputs a Euclidian distanceapproximation as |y|×(1+1/4) when said size relationship is |x|<|y| andis also |y|×(1/2+1/8)<|x|<|y|×(1/2+1/4+1/8), and outputs a Euclidiandistance approximation as |y|×(1+1/4+1/8) when said size relationship is|x|<|y| and is also |y|×(1/2+1/4+1/8)<|x|.
 7. The receiving apparatusaccording to claim 5, wherein said Euclidian distance calculationsection: outputs a Euclidian distance approximation as |x| when a sizerelationship of an I-direction distance x and Q-direction distance y forsaid candidate signal point and said reception signal point on an IQplane is |x|>|y| and is also 0<|y|<|x|×(1/4+1/8), outputs a Euclidiandistance approximation as |xy|×(1+1/8) when said size relationship is|x|>|y| and is also |x|×(1/4+1/8)<|y|<|x|×(1/2+1/8), outputs a Euclidiandistance approximation as |x|×(1+1/4) when said size relationship is|x|>|y| and is also |x|×(1/2+1/8)<|y|<|x|×(1/2+1/4+1/8), and outputs aEuclidian distance approximation as |x|×(1+1/4+1/8) when said sizerelationship is |x|>|y| and is also |x|×(1/2+1/4+1/8)<|y|.
 8. Areceiving apparatus that receives modulated signals transmitted from atransmitting apparatus that transmits different modulated signals from aplurality of antennas, said receiving apparatus comprising: a channelfluctuation estimation section that finds a channel estimate of eachmodulated signal; a partial bit demodulation section that demodulatesonly some bits of said modulated signal using a detection methoddifferent from likelihood detection; a signal point reduction sectionthat reduces candidate signal points using demodulated martial bits andsaid channel estimate; and a likelihood detection section that performslikelihood detection using reduced said candidate signal points and areceived baseband signal, wherein said likelihood detection section:performs likelihood detection based on a Manhattan distance between saidcandidate signal point and a reception signal point of said receivedbaseband signal; performs likelihood detection, taking a Euclidiandistance approximation to be |y| when a size relationship of anI-direction distance x and Q-direction distance y for said candidatesignal point and said reception signal point on an IQ plane is |x|<|y|and is also 0<|x|<|y|×(1/4+1/8), taking a Euclidian distanceapproximation to be |y|×(1+1/8) when said size relationship is |x|<|y|and is also |y|×(1/4+1/8)<|x|<|y|×(1/2+1/8), taking a Euclidian distanceapproximation to be |y|×(1+1/4) when said size relationship is |x|<|y|and is also |y|×(1/2+1/8)<|x|<|y|×(1/2+1/4+1/8), and taking a Euclidiandistance approximation to be |y|×(1+1/4+1/8) when said size relationshipis |x|<|y| and is also |y|×(1/2+1/4+1/8)<|x|; and performs likelihooddetection, taking a Euclidian distance approximation to be |x| when asize relationship of an I-direction distance x and Q-direction distancey for said candidate signal point and said reception signal point on anIQ plane is |x|>|y| and is also 0<|y|<|x|×(1/4+1/8), taking a Euclidiandistance approximation to be |x(1+1/8) when said size relationship is|x|>|y| and is also |x|×(1/4+1/8)<|y|<|x|×(1/2+1/8), taking a Euclidiandistance approximation to be |x|×(1+1/4) when said size relationship is|x|>|y| and is also |x|×(1/2+1/8)<|y|<|x|×(1/2+1/4+1/8), and taking aEuclidian distance approximation to be |x|×(1+1/4+1/8) when said sizerelationship is |x|>|y| and is also |x|×(1/2+1/4+1/8)<|y|.